A mosquito under the microscope.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Many women want non-hormonal birth control. A 22-year-old's findings were used to launch a company that could, within the decade, bring a new kind of contraceptive to the marketplace.
Now a scientist with NextVivo, Shrestha is not directly involved in the development of the contraceptive that is based on her research. But, back in 2016 when she was going through her own problems with hormonal contraceptives, she “was very personally invested” in her research project, Shrestha says. She was coping with a long list of negative effects from an implanted hormonal IUD. According to the Mayo Clinic, those can include severe pelvic pain, headaches, acute acne, breast tenderness, irregular bleeding and mood swings. After a year, she had the IUD removed, but it took another full year before all the side effects finally subsided; she also watched her sister suffer the “same tribulations” after trying a hormonal IUD, she says.
For contraceptive use either daily or monthly, Shrestha says, “You want the antibody to be very potent and also cheap.” That was her goal when she launched her study.
Shrestha unshelved antibody research that had been sitting idle for decades. It was in the late 80s that scientists in Japan first tried to develop anti-sperm antibodies for contraceptive use. But, 35 years ago, “Antibody production had not been streamlined as it is now, so antibodies were very expensive,” Shrestha explains. So, they shifted away from birth control, opting to focus on developing antibodies for vaccines.
Over the course of the last three decades, different teams of researchers have been working to make the antibody more effective, bringing the cost down, though it’s still expensive, according to Shrestha. For contraceptive use either daily or monthly, she says, “You want the antibody to be very potent and also cheap.” That was her goal when she launched her study.
The problem
The problem with contraceptives for women, Shrestha says, is that all but a few of them are hormone-based or have other negative side effects. In fact, some studies and reports show that millions of women risk unintended pregnancy because of medical contraindications with hormone-based contraceptives or to avoid the risks and side effects. While there are about a dozen contraceptive choices for women, there are two for men: the condom, considered 98% effective if used correctly, and vasectomy, 99% effective. Neither of these choices are hormone-based.
On the non-hormonal side for women, there is the diaphragm which is considered only 87 percent effective. It works better with the addition of spermicides — Nonoxynol-9, or N-9 — however, they are detergents; they not only kill the sperm, they also erode the vaginal epithelium. And, there’s the non-hormonal IUD which is 99% effective. However, the IUD needs to be inserted by a medical professional, and it has a number of negative side effects, including painful cramping at a higher frequency and extremely heavy or “abnormal” and unpredictable menstrual flows.
The hormonal version of the IUD, also considered 99% effective, is the one Shrestha used which caused her two years of pain. Of course, there’s the pill, which needs to be taken daily, and the birth control ring which is worn 24/7. Both cause side effects similar to the other hormonal contraceptives on the market. The ring is considered 93% effective mostly because of user error; the pill is considered 99% effective if taken correctly.
“That’s where we saw this opening or gap for women. We want a safe, non-hormonal contraceptive,” Shrestha says. Compounding the lack of good choices, is poor access to quality sex education and family planning information, according to the non-profit Urban Institute. A focus group survey suggested that the sex education women received “often lacked substance, leaving them feeling unprepared to make smart decisions about their sexual health and safety,” wrote the authors of the Urban Institute report. In fact, nearly half (45%, or 2.8 million) of the pregnancies that occur each year in the US are unintended, reports the Guttmacher Institute. Globally the numbers are similar. According to a new report by the United Nations, each year there are 121 million unintended pregnancies, worldwide.
The science
The early work on antibodies as a contraceptive had been inspired by women with infertility. It turns out that 9 to 12 percent of women who are treated for infertility have antibodies that develop naturally and work against sperm. Shrestha was encouraged that the antibodies were specific to the target — sperm — and therefore “very safe to use in women.” She aimed to make the antibodies more stable, more effective and less expensive so they could be more easily manufactured.
Since antibodies tend to stick to things that you tell them to stick to, the idea was, basically, to engineer antibodies to stick to sperm so they would stop swimming. Shrestha and her colleagues took the binding arm of an antibody that they’d isolated from an infertile woman. Then, targeting a unique surface antigen present on human sperm, they engineered a panel of antibodies with as many as six to 10 binding arms — “almost like tongs with prongs on the tongs, that bind the sperm,” explains Shrestha. “We decided to add those grabbers on top of it, behind it. So it went from having two prongs to almost 10. And the whole goal was to have so many arms binding the sperm that it clumps it” into a “dollop,” explains Shrestha, who earned a patent on her research.
Suruchi Shrestha works in the lab with a colleague. In 2016, her research on antibodies for birth control was inspired by her own experience with side effects from an implanted hormonal IUD.
UNC - Chapel Hill
The sperm stays right where it met the antibody, never reaching the egg for fertilization. Eventually, and naturally, “Our vaginal system will just flush it out,” Shrestha explains.
“She showed in her early studies that [she] definitely got the sperm immotile, so they didn't move. And that was a really promising start,” says Jasmine Edelstein, a scientist with an expertise in antibody engineering who was not involved in this research. Shrestha’s team at UNC reproduced the effect in the sheep, notes Edelstein, who works at the startup Be Biopharma. In fact, Shrestha’s anti-sperm antibodies that caused the sperm to agglutinate, or clump together, were 99.9% effective when delivered topically to the sheep’s reproductive tracts.
The future
Going forward, Shrestha thinks the ideal approach would be delivering the antibodies through a vaginal ring. “We want to use it at the source of the spark,” Shrestha says, as opposed to less direct methods, such as taking a pill. The ring would dissolve after one month, she explains, “and then you get another one.”
Engineered to have a long shelf life, the anti-sperm antibody ring could be purchased without a prescription, and women could insert it themselves, without a doctor. “That's our hope, so that it is accessible,” Shrestha says. “Anybody can just go and grab it and not worry about pregnancy or unintended pregnancy.”
Her patented research has been licensed by several biotech companies for clinical trials. A number of Shrestha’s co-authors, including her lab advisor, Sam Lai, have launched a company, Mucommune, to continue developing the contraceptives based on these antibodies.
And, results from a small clinical trial run by researchers at Boston University Chobanian & Avedisian School of Medicine show that a dissolvable vaginal film with antibodies was safe when tested on healthy women of reproductive age. That same group of researchers last year received a $7.2 million grant from the National Institute of Health for further research on monoclonal antibody-based contraceptives, which have also been shown to block transmission of viruses, like HIV.
“As the costs come down, this becomes a more realistic option potentially for women,” says Edelstein. “The impact could be tremendous.”
This article was first published by Leaps.org in December, 2022. It has been lightly edited with updates for timeliness.
When all traditional therapeutic approaches fail for alcohol abuse disorder, a radical gene therapy might be something to try in the future.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
