Exploring the origins of life

Catalytic molecules can form metabolically active clusters by creating and following concentration gradients -- this is the result of a new study by scientists from the Max Planck Institute for Dynamics and Self-Organization (MPI-DS). Their model predicts the self-organization of molecules involved in metabolic pathways, adding a possible new mechanism to the theory of the origin of life. The results can help to better understand how molecules participating in complex biological networks can form dynamic functional structures, and provide a platform for experiments on the origins of life.

One possible scenario for the origin of life is the spontaneous organization of interacting molecules into cell-like droplets. These molecular species would form the first self-replicating metabolic cycles, which are ubiquitous in biology and common throughout all organisms. According to this paradigm, the first biomolecules would need to cluster together through slow and overall inefficient processes. Such slow cluster formation seems incompatible with how quickly life has appeared. Scientists from the department of Living Matter Physics from MPI-DS have now proposed an alternative model that explains such cluster formation and thus the fast onset of the chemical reactions required to form life.

"For this, we considered different molecules, in a simple metabolic cycle, where each species produces a chemical used by the next one," says Vincent Ouazan-Reboul, the first author of the study. "The only elements in the model are the catalytic activity of the molecules, their ability to follow concentration gradients of the chemicals they produce and consume, as well as the information on the order of molecules in the cycle," he continues. Consequently, the model showed the formation of catalytic clusters including various molecular species. Furthermore, the growth of clusters happens exponentially fast. Molecules hence can assemble very quickly and in large numbers into dynamic structures.

"In addition, the number of molecule species which participate in the metabolic cycle plays a key role in the structure of the formed clusters," Ramin Golestanian, director at MPI-DS, summarizes: "Our model leads to a plethora of complex scenarios for self-organization and makes specific predictions about functional advantages that arise for odd or even number of participating species. It is remarkable that non-reciprocal interactions as required for our newly proposed scenario are generically present in all metabolic cycles."

In another study, the authors found that self-attraction is not required for clustering in a small metabolic network. Instead, network effects can cause even self-repelling catalysts to aggregate. With this, the researchers demonstrate new conditions in which complex interactions can create self-organized structures.

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Biological network in cells helps body adapt to stresses on health

Every minute of every day, our body adapts to meet the needs of each moment. When we binge on carbs, exercise, or become sick, chemical reactions inside our cells switch on, slow down, or shift strategy so that we have the energy and strength we need.

All this happens without us knowing it, perhaps explaining why so little is understood about how the body senses and responds to these constant demands. Seeking answers to this question, scientists at University of Utah Health led research that opens up a whole new world within our cells. Their study, published in Science, uncovers a vast network of interactions that suggest how cells adjust in real time to withstand stresses on our health.

"We're discovering how nature has evolved to 'drug' its own proteins and pathways," says Jared Rutter, Ph.D., distinguished professor in the Department of Biochemistry at University of Utah and the study's corresponding author. "By following nature's lead, we're learning how to make better therapeutics."

These findings -- and the technology that made them possible -- has become the basis for the biotechnology company Atavistik Bio, co-founded by Rutter. The company is leveraging this new understanding to accelerate drug discovery for metabolic diseases and cancer.

At a more fundamental level, Rutter says, the advance deepens knowledge about how cells and our bodies work.

A New Frontier

The network described in the study represents an underappreciated layer of regulation in cells that comes from an unexpected source. For nearly 20 years, Rutter's lab has researched metabolism, the chemical reactions that produce energy and build essential components to keep cells running smoothly. Their new research finds that intermediate products of those chemical reactions are more than passive building blocks and sources of fuel for cells, as had long been thought.

Instead, these intermediate products, along with other metabolites, make up an expansive web of sentries that monitor the environment and prompt cells to adapt when needed. They do this by interacting with proteins and modifying how they work. Does a big meal pump too many carbs in the body? Or too much fat? Like a railroad switch that guides a train onto a new track, these protein-metabolite interactions shift metabolic operations to break down those nutrients and steady the course.

The study's first author Kevin Hicks, Ph.D., developed a new technology, termed MIDAS, that reveals the enormity of the regulatory network that acts as an interface between environmental cues and cell metabolism, called the protein-metabolite interactome. The highly sensitive technique identified interactions that had never been seen. An analysis of 33 human proteins involved in converting carbohydrates into fuel found 830 interactions with metabolites. Given that there are thousands of proteins in the cell, the full scale of the network is predicted to be much larger.

"It's surprising how little we know about the extent of these interactions," Hicks says. "We are pushing our understanding of the biological network in new directions."

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