Tyler O'Neal, Staff Editor INDUSTRY

Viral sequencing can reveal how SARS-CoV-2 spreads, evolves

Science "Viewpoint" article stresses importance of sequencing data to control COVID-19 pandemic

The emergence of SARS-CoV-2 virus variants that are adding twists in the battle against COVID-19 highlight the need for better genomic monitoring of the virus, says Katia Koelle, associate professor of biology at Emory University.

"Improved genomic surveillance of SARS-CoV-2 across states would really help us to better understand how the virus causing the pandemic is evolving and spreading in the United States," Koelle says. "More federal funding is needed, along with centralized standards for sample collection and genetic sequencing. Researchers need access to such metadata to better track how the virus is spreading geographically, and to identify any new variants that may make it harder to control, so that health officials can respond more quickly and effectively." {module INSIDE STORY}

Koelle studies the interplay between viral evolution and the epidemiological spread of viral infectious diseases. She is senior author of a "Viewpoint" article just published in the journal Science on the importance of SARS-CoV-2 sequencing to control the COVID-19 pandemic.

Michael Martin, a PhD student in Emory's Population, Biology and Ecology Program and a member of Koelle's lab, is first author of the Science article. David VanInsberghe, a post-doctoral fellow in Koelle's lab, is co-author.

"Research into SARS-CoV-2 has been going at lightning speed," Martin says. "This acceleration has provided us with one of the largest datasets ever so quickly assembled for a disease. We've learned a lot so far about how this virus spreads and adapts, but we still have many blind spots that need to be addressed."

The article summarizes key insights about SARS-CoV-2 that have already been gained by sequencing of its genome from individual patient samples. It also cites challenges that remain, including the collection and integration of metadata into genetic analyses and the need for the development of more efficient and scalable computational methods to apply to hundreds of thousands of genomes.

A genome is an organism's genetic material. Human genomes are made up of double-stranded DNA, coded in four different nucleotide base letters. A single human genome consists of more than 3 billion base pairs. In contrast, the genome of coronaviruses, including SARS-CoV-2, are made of RNA, which can have a simpler structure than DNA. The SARS-CoV-2 genome, for instance, consists of a single RNA strand that is only 30,000 letters long. Sequencing is a technique that provides a read-out of these letters.

If the SARS-CoV-2 virus is found in a sample swabbed from someone's nose or mouth, it confirms the likelihood that the person is carrying the virus, whether they have symptoms of COVID-19 or not. The virus in the sample can also be sequenced.

"Sequencing the virus is like fingerprinting it," Koelle explains. "And based on how close the fingerprints match between samples -- that is, how close they are genetically -- you can at times learn who is infecting whom. Analyzing sequences from samples taken from infected individuals in a given region over time can provide even more information."

Analyses of SARS-CoV-2 sequencing data have enabled researchers to estimate the timing of SARS-CoV-2 spillover into humans; identify some of the transmission routes in its global spread; determine infection rates and how they change within a region; and identify the emergence of some new variants of concern.

Viral genomes can mutate during replication, changing letters as they spread to new people. Most of these random mutations will likely not affect the transmissibility or virulence of a virus -- but a few may make it even more difficult to fight. Early evidence, for instance, suggests that a SARS-CoV-2 variant that recently emerged in the UK may be more easily transmitted and potentially more severe. A South African variant shows signs that it may reduce the efficacy of existing vaccines, while a variant first detected in Brazil also contains mutations that health officials worry may make the virus spread more quickly.

"It can be difficult to identify which variants actually change how the virus replicates, spreads and causes disease because of confounding factors," Martin explains. "If a variant spreads more quickly, for instance, you have to tease apart whether that was due to it becoming more transmissible or if someone who was infected with it attended a large gathering."

The better data researchers have, the faster they can solve such puzzles, he adds.

Technological advances during recent years have made it more efficient and less costly to generate sequencing data. Barely a year after it emerged, more than 400,000 sequences of SARS-CoV-2 are now available in public databases, such as the GISAID platform which was launched in 2008 to share information among National Influenza Centers for the WHO Global Influenza Surveillance and Response System.

"A large chunk of the public sequencing data for SARS-CoV-2 has come out of the UK," Koelle notes. "That's because the British government has an initiative to do high-density sampling of the SARS-CoV-2 genome."

The rich data set from the UK helped identify the emergence of the variant in Britain that is spreading rapidly. "There might be other variants of concern emerging in other places around the world besides the ones already identified, but we just don't know because we don't have as good of surveillance in those locations," Koelle says.

"While the United States has been slow in efforts to sequence SARS-CoV-2 from samples across the nation, there are several excellent viral sequencing efforts and phylogenetic analyses, primarily driven by academic researchers, that have helped to understand SARS-CoV-2 transmission more locally," Koelle says. "We have the expertise in the U.S., but the effort is more piecemeal."

"We need a coordinated, nationally standardized program to do widespread sequencing of SARS-CoV-2 in the United States," Martin says. "Much of the data collected now just has a state identifier but we need greater resolution while also protecting patient privacy. More county-level identifiers, for instance, would be one way to greatly improve the quality and the depth of the data."

Once the COVID-19 pandemic ebbs, it's important to continue to build the national infrastructure and systems for infectious disease surveillance -- including viral sequencing -- and to keep it in place, both researchers stress.

"There will be more infectious disease pandemics, and we need to be better prepared," Martin says.

Miki's models show galactic collisions can starve massive black holes

Tyler O'Neal, Staff Editor INDUSTRY

It was previously thought that collisions between galaxies would necessarily add to the activity of the massive black holes at their centers. However, researchers have performed the most accurate simulations of a range of collision scenarios and have found that some collisions can reduce the activity of their central black holes. The reason is that certain head-on collisions may in fact clear the galactic nuclei of the matter which would otherwise fuel the black holes contained within.

When you think about gargantuan phenomena such as the collision of galaxies, it might be tempting to imagine it as some sort of cosmic cataclysm, with stars crashing and exploding, and destruction on an epic scale. But actually, it is closer to a pair of clouds combining, usually, a larger one absorbing a smaller one. It's unlikely any stars within them would collide themselves. But that said, when galaxies collide, the consequences can be enormous. Artist's impression of gas being pulled away from a galactic nucleus. CREDIT © 2021 Miki et al.{module INSIDE STORY}

Galaxies collide in different ways. Sometimes a small galaxy will collide with the outer part of a larger one and either pass through or merge, in either case exchanging a lot of stars along the way. But galaxies can also collide head-on, where the smaller of the two will be torn apart by overpowering tidal forces of the larger one. It's in this scenario that something very interesting can happen within the galactic nucleus.

"At the heart of most galaxies lies a massive black hole, or MBH," said Research Associate Yohei Miki from the University of Tokyo. "For as long as astronomers have explored galactic collisions, it has been assumed that a collision would always provide fuel for an MBH in the form of matter within the nucleus. And that this fuel would feed the MBH, significantly increasing its activity, which we would see as ultraviolet and X-ray light amongst other things. However, we now have good reason to believe that this sequence of events is not inevitable and that in fact, the exact opposite might sometimes be true." 

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It seems logical that a galactic collision would only increase the activity of an MBH, but Miki and his team were curious to test this notion. They constructed highly detailed models of galactic collision scenarios and ran them on supercomputers. The team was pleased to see that in some circumstances, an incoming small galaxy might actually strip away the matter surrounding the MBH of the larger one. This would reduce instead of increasing its activity.

"We computed the dynamic evolution of the gaseous matter which surrounds the MBH in a torus, or donut, shape," said Miki. "If the incoming galaxy accelerated this torus above a certain threshold determined by properties of the MBH, then the matter would be ejected and the MBH would be starved. These events can last in the region of a million years, though we are still unsure about how long the suppression of MBH activity may last."

This research could help us understand the evolution of our own Milky Way. Astronomers are confident our galaxy has collided with many smaller ones before. Paper: http://dx.doi.org/10.1038/s41550-020-01286-9

Simulations uncover seizures’ complex underlying domino-like transient dynamics in epilepsy

Tyler O'Neal, Staff Editor INDUSTRY

Epilepsy, a neurological disease that causes recurring seizures with a wide array of effects, impacts approximately 50 million people across the world. This condition has been recognized for a long time — written records of epileptic symptoms date all the way back to 4000 B.C.E. But despite this long history of knowledge and treatment, the exact processes that occur in the brain during a seizure remain elusive. 

Scientists have observed distinctive patterns in the electrical activity of neuron groups in healthy brains. Networks of neurons move through states of similar behavior (synchronization) and dissimilar behavior (desynchronization) in a process that is associated with memory and attention. But in a brain with a neurological disorder like epilepsy, synchronization can grow to a dangerous extent when a collection of brain cells begins to emit excess electricity. “Synchronization is thought to be important for information processing,” Jennifer Creaser of the University of Exeter said. “But too much synchronization—such as what occurs in epileptic seizures or Parkinson’s disease—is associated with disease states and can impair brain function.” 

Measurements of epileptic seizures have revealed that desynchronization in brain networks often occurs before or during the early stages of a seizure. As the seizure progresses, networks become increasingly more synchronized as additional regions of the brain get involved, leading to high levels of synchronization towards the seizure’s end. Understanding the interactions between the increased electrical activity during a seizure and changes in synchronization is an important step towards improving the diagnosis and treatment of epilepsy.

Jennifer Creaser, Peter Ashwin (University of Exeter), and Krasimira Tsaneva-Atanasova (the University of Exeter, Technical University of Munich, and Bulgarian Academy of Sciences) explored the mechanisms of synchronization that accompany seizure onset in a paper published in December in the SIAM Journal on Applied Dynamical Systems. In their study—which took place at the Engineering and Physical Science Research Council’s Centre for Predictive Modelling in Healthcare at the University of Exeter and the University of Birmingham—the researchers used mathematical modeling to explore the interplay between groups of neurons in the brain that leads to transitions in synchronization changes during seizure onset. “Although this is a theoretical study of an idealized model, it is inspired by challenges posed by understanding transitions between healthy and pathological activity in the brain,” Ashwin said. {module INSIDE STORY}

The authors utilize an extended version of an existing mathematical model that represents the brain as a network connecting multiple nodes of neuron groups. The model network consists of bistable nodes, meaning that each node is able to switch between two stable states: resting (a quiescent state) and seizure (an active and oscillatory state). These nodes remain in their current state until they receive a stimulus that gives them a sufficient kick to escape to the other state. In the model, this stimulus comes from other connected nodes or appears in the form of “noise” — outside sources of neural activity, such as endocrine responses that are associated with an emotional state or physiological changes due to disease. 

The influence between neighboring nodes is governed by a coupling function that represents the way in which the nodes in the network communicate with each other. The first of the two possible types of coupling is amplitude coupling, which is governed by the “loudness” of the neighboring nodes. The second is phase coupling, which is related to the speed at which the neighbors are firing. Although the researchers needed to utilize a simple formulation on a small network to even make their analysis possible—a more complex and realistic system would be too computationally taxing—they expected their model to exhibit the same types of behaviors that clinical recordings of real brain activity have revealed. 

The nodes in the modeled system all begin in the healthy resting state. In previous research, the authors found that adding a small amount of noise to the system caused each node to transition to the active state — but the system’s geometry was such that returning to the resting state took much longer than leaving. Because of this, these escapes can spread sequentially as a “domino effect” when a number of nodes are connected. This leads to a cascade of escapes to the active state—much like a falling line of dominos—that spreads activity across the network. 

Creaser, Ashwin, and Tsaneva-Atanasova’s new paper build upon this previous research on the domino effect to explore the transitions into and out of synchrony that occur during cascades of escapes. The team used their model to identify the circumstances that bring about these changes in synchrony and investigate how the type of coupling in a network affects its behavior. 

When the model incorporated only amplitude coupling, it exhibited a new phenomenon in which the domino effect could accelerate or decelerate. However, this effect had no bearing on synchronization changes in the network; all of the nodes started and remained synchronized. But when the model incorporated more general amplitude and phase coupling, the authors found that the nodes’ synchrony could change between consecutive escapes during the domino effect. They then determined which conditions would cause changes in synchrony under phase-amplitude coupling. This change in synchrony throughout the sequence of escapes was the study’s most novel result. 

The results of this work could facilitate further studies on seizures and their management. “The mathematical modeling of seizure initiation and propagation can not only help to uncover seizures’ complex underlying mechanisms, but also provide a means for enabling in silico experiments to predict the outcome of manipulating the neural system,” Tsaneva-Atanasova said. Understanding the interplay between synchronized and desynchronized dynamics in brain networks could help identify clinically-relevant measures for seizure treatment. For example, Creaser and Tsaneva-Atanasova recently served as the lead and senior author, respectively, on a paper that utilized a simpler version of the model to classify patterns of seizure onset that were recorded in a clinical setting. In the future, these kinds of modeling studies may lead to the personalization of seizure identification and treatment for individuals with epilepsy.