“We are perhaps more alert than ever to the catastrophic impacts of infectious disease in both humans and animals. Our task now is to begin to understand what drives these events, particularly in species like marine mammals, where our knowledge is even more limited,” said Claire Sanderson, a research associate in the Department of Fish and Wildlife Conservation within the College of Natural Resources and Environment and the research coordinator of the Center for African Resources: Animals, Communities, and Land Use (CARACAL).

In 2000, over 10,000 endangered Caspian seals died in less than a four-month span. Researchers later discovered that the culprit behind this devastating mass mortality event was canine distemper virus.

Infectious disease-induced mass mortality events are known to afflict a variety of species, including invertebrates, birds, fish, and both land and aquatic mammals. However, these events in aquatic mammals are understudied compared to their land-dwelling counterparts.

To help fill in the knowledge gaps, Sanderson worked with Kathleen Alexander, a professor in the Department of Fish and Wildlife Conservation and co-founder of CARACAL, to evaluate the factors influencing the occurrence of these events in marine mammals. They published their findings in Global Change Biology.

Sanderson and Alexander defined a mass mortality event as a “devastating number of fatalities within the same species over a brief period of time,” which is also dependent on the species’ conservation status and population size. Beyond the deaths of individual animals, mass mortality events can initiate a cascade of events with severe ecological repercussions. Affected populations are often at a greater risk of local extinction, and changes in community structure can upset an ecosystem’s balance.

By conducting extensive literature searches, Sanderson and Alexander discovered that infectious disease-induced mass mortality events occurred in 14 percent of marine mammal species between 1955 and 2018. Viruses were responsible for 72 percent of these events and caused 20 times the number of deaths than bacterial outbreaks. Specifically, morbillivirus and influenza A outbreaks were the most commonly recorded. Due to their life cycles, both viruses can infect multiple hosts since they have the potential to be transmitted between various species.

To determine which factors influenced the occurrence of infectious disease-induced mass mortality events in marine mammals, Sanderson and Alexander evaluated key life history traits and environmental variables.

To evaluate the role of life history traits, they focused on sociality, trophic level (a species’ position on the food chain), and habitat breadth. While sociality and trophic level did not appear to be associated with mass mortality events in marine mammals, habitat breadth did. More than half of the species experiencing mass mortality events were pinnipeds, an order of semi-aquatic species that includes seals, sea lions, and walruses.

“Pinnipeds bridge the terrestrial and aquatic interfaces and may have a greater exposure to pathogens that occur across these land types,” Sanderson said. For instance, seals and sea lions are often observed on beaches, rocky shorelines, and docks, but they also spend a significant part of their lives in water.

In contrast to life history traits, environmental variables — such as season and sea surface temperature anomalies — were significantly associated with disease outbreaks in marine mammals.

One of the most indicative measures of climate change is fluctuating sea surface temperatures. Sanderson and Alexander found that 61 percent of mass mortality events in marine mammals occurred during periods in which regional sea surface temperature anomalies occurred. The study suggested that the incident rate of an infectious disease-induced mass mortality event increases by nearly 12 percent for “every unit increase in global sea surface temperature anomalies.”

“As climate change intensifies, this may set off a complex chain of events that dramatically alter these ecosystems, affecting marine populations living in these environments,” said Alexander an affiliated faculty member of the Fralin Life Sciences Institute. Climate change effects on sea ice and ocean salinity also have implications for the spread of disease.

In polar regions, sea ice is melting more quickly due to increasing air temperature, but various species of pinnipeds need sea ice to give birth, rest, mate, escape predators, and molt. As the ice melts away, pinnipeds are forced to congregate onto smaller, shrinking ice sheets. At higher densities, increased contact between individuals can allow pathogens to be transmitted more readily through a population and region.

The chemical balance of the ocean is also changing. In polar regions, melting sea ice dilutes the salty ocean water in a process called freshening. This can result in decreased prey abundance, since species like fish, mollusks, and crustaceans are sensitive to small changes in the environment. With less food to feed on, marine mammals may suffer from nutritional stress, resulting in decreased immune function and a greater susceptibility to disease.

“Marine mammals represent important sentinels of aquatic health by providing information essential for managing threats to these vulnerable ecosystems,” said Sanderson. “Addressing the root causes of climate change will be of critical importance as we chart a path forward in managing these species.”

Of all the marine mammal species that suffered an infectious disease-induced mass mortality event, the study found that 37 percent were listed as endangered or vulnerable to extinction.

“This work underscores the critical threat infectious disease can pose to marine species and the potential importance of climate change as a wide reaching driver of this process. Here, our role is complex as we contribute directly to our changing climate, increasing pathogen pollution, and habitat degradation. But we also have the power to change things, address climate change, protect species and environments through effort and innovation — humanity at its best,” Alexander said.

The National Science Foundation funded this research through the multi-agency Ecology and Evolution of Infectious Diseases program.

The work, published June 18 in the Lancet journal EBioMedicine, offers a new method for developing safer medicines by identifying potential adverse reactions before drug candidates reach human clinical trials or enter the market as approved medicines.

The findings also offer insights into how the human body responds to drug compounds at the molecular level in both desired and unintended ways.

“Machine learning is not a silver bullet for drug discovery, but I do believe it can accelerate many different aspects in the difficult and long process of developing new medicines,” said paper co-first author Robert Ietswaart, research fellow in genetics in the lab of Stirling Churchman in the Blavatnik Institute at HMS. Churchman was not involved in the study.

“Although it cannot predict all possible adverse effects, we hope that our work will help researchers spot potential trouble early on and develop safer drugs in the future,” Ietswaart said.

Drug side effects, technically known as adverse drug reactions, range from mild to fatal. They may occur either when taking a drug as prescribed or as a result of incorrect dosages, interaction of multiple medicines or off-label use (taking a drug for something other than what it was approved for). Adverse drug reactions are responsible for 2 million U.S. hospitalizations each year, according to the Department of Health and Human Services, and occur during 10 to 20 percent of hospitalizations, according to the Merck Manuals.

Researchers and health care providers have applied many tactics over the decades to avoid or at least minimize adverse drug reactions. But because a single drug often interacts with multiple proteins in the body — not always limited to the intended targets — it can be hard to predict what, if any, side effects a medicine may generate. And if a drug does end up causing an adverse reaction, it can be hard to identify which of its protein targets could be responsible.

In the new study, researchers took one existing database of reported adverse drug reactions and another database of 184 proteins that specific drugs are known to often interact with. Then they constructed a computer algorithm to connect the dots.

“Learning” from the data, the algorithm unearthed 221 associations between individual proteins and specific adverse drug reactions. Some were known and some were new.

The associations indicated which proteins likely represent drug targets that contribute to particular side effects and which others may be innocent bystanders.

Based on what it has already “learned,” and strengthened by any new data that researchers feed it, the program may help doctors and scientists predict whether a new drug candidate is likely to cause a certain side effect on its own or when combined with particular medicines. The algorithm can help with these predictions before a drug is tested in humans, based on lab experiments that reveal which proteins the drug interacts with.

The hope is to raise the likelihood that a drug candidate will prove safe for patients before and after it reaches the market.

“This could reduce the risks that study participants face during the first in-human clinical trials and minimize risks for patients if a drug gains FDA approval and enters clinical use,” said Ietswaart.

Hack your side effects

The project was born at a quantitative science hackathon organized by Novartis Institutes for BioMedical Research (NIBR) in 2018.

Laszlo Urban, global head of preclinical secondary pharmacology at NIBR, presented on some of the problems his team faces when assessing the safety of new drug candidates. A group of Boston-area graduate students and postdocs at the hackathon jumped to apply their knowledge of data science and machine learning.

Most of the time, projects from the hackathon end as learning exercises, said Urban. On this rare occasion, however, a strong and lasting interaction of inspired scientists from different institutions resulted in a novel application published in a highly respected journal, he said.

Four members of the original hackathon group became co-first authors of the paper: Ietswaart at HMS, Seda Arat from The Jackson Laboratory, Amanda Chen of MIT and Saman Farahmand from the University of Massachusetts Boston. Arat is now at Pfizer. Another team member, Bumjun Kim of Northeastern University, is a co-author. Urban became senior author of the paper.

To tackle the problem, the team constructed its machine learning algorithm and applied it to two large data sets: one from Novartis with information about the proteins that each of 2,000 drugs interact with and one from the FDA with 600,000 physician reports of adverse drug reactions in patients.

The algorithm generated statistically robust information about how individual proteins contribute to documented adverse reactions, said Ietswaart.

“It suggests the physiological response to perturbing a particular protein — or the gene that makes it — at the molecular level,” he said.

Many of the results supported previous observations, such as that binding to the protein hERG can cause cardiac arrhythmias. Findings like this strengthened the researchers’ confidence that the algorithm was performing well.

Other results, however, were unexpected.

For instance, the algorithm suggested that the protein PDE3 is associated with over 40 adverse drug reactions. Doctors and researchers have known for years that PDE3 inhibitors — common anti-clotting treatments for acute heart failure, stroke prevention and a heart attack complication known as cardiogenic shock — can cause arrhythmias, low platelet counts and elevated levels of enzymes called transaminases, a possible indicator of liver damage. But it wasn’t known that targeting PDE3 might raise the risk of so many other side effects, including some related to the muscles, bones, connective tissue, kidneys, urinary tract and ear.

Into the future

The algorithm also offered predictions on the likelihood that a particular drug would cause a certain adverse reaction.

How accurate were those new predictions? To find out, the researchers fed their algorithm updated information. Until then, the program had learned from adverse drug reactions reported through 2014. The team added reports gathered from 2014 through 2019, some of which revealed side effects that hadn’t been observed before from particular drugs.

Sure enough, many of the algorithm’s previously unproven predictions matched the recent real-world reports.

“What seemed like false-positive predictions proved not to be false at all when the new reports became available,” said Ietswaart.

To make extra certain that the algorithm is reliable, the team compared its results to drug labels, conducted text mining of the scientific literature and used other validation techniques.

Although the researchers strengthened the model as much as they could, it still assesses less than 1 percent of the 20,000 genes in the human genome.

“Our work is by no means a complete understanding of adverse drug events because many other genes and proteins might contribute for which no assay is available or no drugs have been tested,” said Ietswaart.

Scientists can use, improve and build upon the model, which is posted for free online at https://github.com/samanfrm/ADRtarget.

“This work has been a collaborative ‘open science’ spirit and team effort,” said Ietswaart and Urban.

In a paper published in the Journal of Child Psychology and Psychiatry, researchers analysed self-reported sleep quality and quantity from teenagers and found that there was a significant relationship between poor sleep and mental health issues.

The team, based at the University of Reading, and Goldsmiths and Flinders Universities found that among the 4790 participants, those who experienced depression reported both poor quality and quantity of sleep, while those with anxiety had poor quality of sleep only, compared to those teenagers who took part who didn’t report anxiety or depression.

Dr Faith Orchard, a Lecturer in Clinical Psychology at the University of Reading said:

“This latest research is another piece of evidence to show that there is a significant link between sleep and mental health for teenagers. This study highlights that those young people who have experienced depression and anxiety had overwhelmingly experienced poor sleep during their teens.

“What’s noticeable is that the difference in average amount of sleep between those who experienced depression, which amounts to going to sleep 30 minutes later each night compared to other participants. Within the data, there were some participants who reported hugely worse quality and quantity of sleep, and the overall picture highlights that we need to take sleep much more into account when considering support for teenager wellbeing.”

Teens were asked to self-report on sleep quality and quantity over a series of issues, and the researchers found that the control group of teenagers were on average getting around eight hours of sleep a night on school nights and a little over nine and half hours sleep on weekends.

Meanwhile, the group who had a depressive diagnosis were getting less than seven and a half hours sleep on week nights and just over nine hours sleep at weekends.

A co-author, Professor Alice Gregory from Goldsmiths University, said:

“The National Sleep Foundation recommends that adolescents aged between 14-17 years typically need around 8-10 hours of sleep each night. What is notable here is that the group with a diagnosis of depression most clearly fell outside of these recommendations during the week — getting on average 7.25 hours of sleep on each school night.”

The depression group were therefore reporting an average total of 3325 minutes of sleep a week compared to the control group who reported 3597, meaning that the depression group were on average getting 272 minutes or three and a half hours less sleep a week.

While the team noted that although the data was based on self-reporting of sleep and therefore less accurate, the fact of self-reported worse quality and quantity of sleep was still significant.

Dr Orchard said:

“What we are now seeing is that the relationship between sleep and mental health for teenagers is a two way street. While poorer sleep habits are associated with worse mental health, we are also seeing how addressing sleep for young people with depression and anxiety can have a big impact on their wellbeing.

“It’s also important to note that the numbers of young people who report anxiety and depression are still low overall. Good sleep hygiene is important, and if you are concerned about yours or your child’s wellbeing we strongly encourage you to seek support from your doctor, but any short term negative impact on sleep is not a cause for alarm.”

Professor Gregory said:

“The Department for Education is aware of the importance of sleep in children and adolescence — and it is really good news that from September 2020 Statutory Guidance will mean that they will be taught about the value of good quality sleep for many aspects of their lives including their mood.”

Co-author Professor Michael Gradisar from Flinders University, Australia said:

“This longitudinal study confirms what we see clinically — that poor sleep during adolescence can be a ‘fork in the road’, where a teen’s mental health can deteriorate if not treated. Fortunately there are sleep interventions available for schools and individual families — and these can place teens back on the road to healthy sleep..”

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