Tyler O'Neal, Staff Editor PROCESSORS

SISSA scientists show how DNA is unwound in one direction or the other

The “book of life” must be opened to be read. An international research group sheds light on one of the key mechanisms of cell life by joining supercomputer simulations and lab experiments. The research has been published on PNAS.

Joining computer simulations and lab experiments, an international research group sheds light on one of the key mechanisms of cell life{module In-article} 

From home to office and back. The road is the same, and yet the outbound journey is longer than the inbound journey. Why is that? The reason is the obstacles the car driver usually finds on his way to work, which is absent on the way back. Now, replace the road with DNA strands and you will have grasped the crux of the discovery just published by an international research group on the journal PNAS. The double helix of DNA is subject to the action of specific proteins, called helicases, which have the task of separating the two strands so that the information contained in the genome is made available for a variety of activities that are essential to cell life. In short, DNA is like a book, it needs to be opened to be read! Helicases have the task of opening this “book”. Through this work, the researchers have shown that helicases succeed in carrying out their tasks more easily, and therefore quickly, working on one of the two strands with respect to the other. They explained that the reason lies in the sequence composition of the DNA section. As we know, DNA has four nitrogenous bases, which constitute the alphabet that is used to write our genome. When there are “hindered” bases like adenine and guanine on the strand opposite to that on which the helicase moves, they bump against the helicase and make the process slower. If, on the contrary, there are “small” bases, like cytosine and thymine, the opening process is faster. The scientists infer that this could mean that the genome has yet-another way, which has not been considered until now, of regulating the flow of information: indeed, the genetic information connected with the direction in which the helicase proceeds more slowly will be read less frequently – with possible consequences on the gene expression. The research was initially conducted with supercomputer simulations. The predictions obtained in this manner were then confirmed by experiments. The study emerged from an idea of two SISSA scientists, Professor Giovanni Bussi and Francesco Colizzi, who has since moved to the Institute for Research in Biomedicine (IRB Barcelona), Spain, and has involved the laboratories led by Carlos Penedo and Malcolm White at the University of Saint Andrews, Great Britain, with their collaborators.

First theory and then experiment

“We know that the difficulty in unwinding a double strand of DNA or RNA depends on the strength which binds the two, but that the same double helix was easier to unwind in one direction rather than the other has been a real surprise. We have come to propose this hypothesis through supercomputer simulations and subsequently designed real lab experiments that could prove or disprove the hypothesis. Eventually, the lab experiments proved that the hypothesis was correct”. Thanks to fluorescent tracers, the researchers observed that in the experiment the double helix is processed more easily in one direction rather than the other”. In short, there is one strand and one preferential direction for unwinding DNA according to the message written in it. Scientifically, this is very interesting”. Since the helicase opens the double helix moving on a strand separating it from the opposite one, “we have hypothesized and verified that the speed of this process depends on the composition of the bases of the latter”. If there are many bumpy bases, like guanine and adenine, then the helicase will have a more complex task and proceed more slowly. And vice versa. “What we can say is that the direction with which it is easier to separate a double helix depends on the sequence of bases in a specific region.”

The implications of the discovery

This discovery bears different points of interest. The first, as we said, is connected with the approach that brings together supercomputer simulations and lab experiments in a combination that leads us straight to the frontiers of science.

The second is of a more general nature: “The regulation of all the activities in which DNA is involved is one of the most interesting and crucial issues of biology, in which much more still needs to be discovered. With this study, we put forward a hypothesis that has not been considered so far: namely, that the various types of regulation already known are joined by another which relates specifically to the preferential direction in which a double strand is read, based on the ease of being unwound. There is an additional, more speculative consideration, based on the observation that DNA unwinds in a different way to RNA. “For DNA, we find helicases able to work in both directions. Instead, for RNA, the helicases found in nature follow mostly one-way roads. The hypothesis of the scientists is that the double helix of RNA has some sort of geometrical asymmetry (different to DNA) that, throughout evolution, has dictated the one-way direction of RNA helicases. It can, therefore, be speculated that evolution has selected the helicases that process RNA in the direction in which it is easy to travel. Unfortunately, a hypothesis of this kind cannot be refuted or verified: to do so, it would be necessary to retrace the natural evolution for millions of years, eliminating this preference and seeing what happens! It is definitely fascinating to think that the geometry of the double helix can have deeply influenced the development of molecular machines necessary for life.”

Japanese researchers' cycles of reward discovery leads to new insight into ADHD treatment

Tyler O'Neal, Staff Editor PROCESSORS

Attention-Deficit Hyperactivity Disorder (ADHD) is a widespread condition with complex underlying causes. A stimulant drug called methylphenidate is a common ADHD treatment that impacts the brain’s levels of dopamine, a neurotransmitter involved in systems of reward; however, methylphenidate has a potential for abuse, and its therapeutic effects are poorly understood.

To explore methylphenidate’s varied interactions with dopamine systems in the brain, researchers at the Okinawa Institute of Science and Technology Graduate University (OIST) in collaboration with scientists at the University of Otago and the University of Auckland in New Zealand, investigated the actions of the drug in rats. Using dopamine cell recordings, electrochemical monitoring, and supercomputer modeling, they discovered a type of feedback loop that modulates dopamine levels in the rats’ brains in response to the drug. This regulatory process may shed light on methylphenidate’s therapeutic properties in ADHD. The researchers’ findings are published in Progress in Neurobiology. Professor Jeff Wickens and technician Kavinda Liyanagama study data from experiments looking at dopamine release in rat brains.{module In-article} 

“We know quite a bit about how methylphenidate works at the molecular level, but not how it affects greater neural systems. It’s still a mystery how this drug improves symptoms of ADHD,” said Professor Jeff Wickens of OIST’s Neurobiology Research Unit. “This mystery leads us to explore how different parts of the brain interact to produce therapeutic effects.”

Unlocking the secrets of dopamine

To carry out their research, the international team administered methylphenidate at a concentration of 5.0 mg/kg to a group of adult male rats, while a control group received no drugs. After surgically implanting electrodes in the rats’ brains, the researchers used an electrochemical technique called voltammetry to monitor real-time changes in cellular dopamine concentration in brain regions involved in ADHD. The researchers also took measurements in live brain slices of rats’ midbrains and forebrains.

To help understand the data, the scientists at OIST, including technician Kavinda Liyanagama, designed a supercomputer program to model the effects of methylphenidate on dopamine systems.

Neurons release dopamine in different ways: phasic release is characterized by quick, high-intensity spikes in the neurotransmitter, often in response to motivational stimuli like drugs or sugary treats. Tonic release, on the other hand, refers to slower, more regular firings of dopamine neurons, and is involved in muscle and joint movements.

Wickens and his collaborators initially thought that since methylphenidate blocks the reuptake of dopamine by receptors in the brain, the drug should increase the phasic dopamine signal. Rather, after analyzing their data, the researchers found the opposite: methylphenidate did not increase phasic dopamine. To explain this finding, Wickens suspects that the brain has a remarkably powerful feedback mechanism to keep the brain’s dopamine levels in check, even when reuptake is blocked by methylphenidate.

“When you use methylphenidate in the intact brain there’s a neural regulation mechanism to compensate for the direct effects of the drug,” said Wickens. “Methylphenidate’s therapeutic effects could be indirect consequences of this feedback loop.”

The computer modeling suggests that methylphenidate primarily impacts the tonic dopamine signal. Shifts in tonic dopamine signaling may activate dopamine receptors in ways that improve the symptoms of ADHD.

Wickens acknowledges that the study was conducted in healthy rats. The group’s next step is to look at this feedback loop in animal models of ADHD.

Also, Wickens suspects that ADHD is a collection of disorders, warranting combination therapies. Thus, he hopes to explore the mechanisms of other treatments, too.

New UH developed AI deep learning model allows earlier, more accurate ozone warnings

Tyler O'Neal, Staff Editor PROCESSORS

Researchers from the University of Houston have developed an artificial intelligence-based ozone forecasting system, which would allow local areas to predict ozone levels 24 hours in advance.

That would improve health alerts for people at heightened risk of developing problems because of high ozone levels.

Yunsoo Choi, associate professor in the Department of Earth and Atmospheric Sciences and corresponding author for a paper explaining the work, said they built an artificially intelligent model using a convolutional neural network, which is able to take information from current conditions and accurately predict ozone levels for the next day. The work was published in the journal Neural Networks.

“If we know the conditions of today, we can predict the conditions of tomorrow,” Choi said. Yunsoo Choi, left, associate professor in the Department of Earth and Atmospheric Sciences at UH, and Ph.D. student Alqamah Sayeed explain a new model to better predict ozone levels.{module In-article} 

Ozone is an unstable gas, formed by a chemical reaction when sunlight combines with nitrogen oxides (NOx) and volatile organic compounds, both of which are found in automobile and industrial emissions. It can cause respiratory problems in people, and those especially susceptible to ozone – including people with asthma, the elderly and young children – are advised to reduce their exposure when ozone levels are high.

Alqamah Sayeed, first author on the paper and a Ph.D. student in Choi’s Air Quality Forecasting and Modeling Lab, said most current ozone forecasting models don’t incorporate artificial intelligence and can take several hours to predict future ozone levels, rather than just a few seconds for the new model. They also are less accurate; the researchers reported their model correctly predicted ozone levels 24 hours in advance between 85% and 90% of the time.

A key difference, Choi said, is the use of convolutional neural networks, networks capable of “sweeping” data and using that to form assumptions based on what it has learned. The convolutional networks are generally used to improve imaging resolution, he said. Choi and Sayeed said using the networks to extract information and then using artificial intelligence in order to make predictions from that data is a new application, illustrating the strength of the networks’ ability to gather information and make inferences based upon that information.

The researchers used meteorological and air pollution data collected at 21 stations in Houston and elsewhere in Texas by the Texas Commission on Environmental Quality, representing conditions between 2014 and 2017. Sayeed said they programmed the convolutional neural networks using meteorological data – temperature, barometric pressure, wind speed, and other variables – for each day, and added ozone measurements from each station for 2014, 2015 and 2016.

To test their belief that the model would be able to predict ozone levels given meteorological conditions from the previous day, they added weather data for 2017 and checked the forecasts the network produced for accuracy.

The model’s forecasts reached 90% accuracy, and Choi said it will become more accurate over time, as the network continues to learn.

Although the tests were done using Texas data, the researchers said the model could be used anywhere in the world. “The U.S. is geographically different from East Asia,” Choi said, “but the physics and chemistry of ozone creation are the same.”

Sayeed said the researchers are currently working to expand the model to include predictions of other types of pollutants, including particulate matter, as well as to extend the time period beyond 24 hours.

In addition to Sayeed and Choi, researchers on the project include Ebrahim Eslami, Yannic Lops, Anirban Roy and Jia Jung, all with the Department of Earth and Atmospheric Sciences in the UH College of Natural Sciences and Mathematics.