Kelsey Coffman, University of Tennessee

Take a stroll along one of the beaches on Hawaii Island in late summer, and you’ll likely stumble upon almond-shaped fruits lying in the sand. Known as false kamani nuts, or tropical almonds, they fall from tall, shady Terminalia catappa trees that line the many picturesque ocean views on the island.

But what may not be clear to the casual beachgoer is that there’s a fight for survival occurring within the flesh of these unassuming fruits. Tropical almonds are one of many active battlegrounds in a war between a global agricultural pest, a parasitic wasp and a beneficial virus.

As an entomologist who studies insect viruses, I want to untangle the complex interactions that insects have evolved with microbes. The findings might help researchers tackle global food security issues.

A global pest challenge

At the center of this conflict are invasive fruit flies in the family Tephritidae, many of which have spread across the globe and wreak havoc on hundreds of commercial fruits and vegetables.

In Hawaii, several species of tephritid fruit fly invaded, starting in the late 1800s. They have caused major economic losses to fruit production across the islands. Scientists and fruit growers have undertaken enormous efforts to control these flies since their initial introductions, but they remain a serious economic problem.

One reliable method of control has been to release tiny insects called parasitoid wasps into the wild that can hunt down immature fruit flies and target them for annihilation. The term parasitoid describes an organism that spends its development as a parasite and eventually kills its host.

Parasitoid wasps use an elongated stinger, known as an ovipositor, to drill into fruits where flies are developing and pierce the fly’s body to lay an egg within. Wasp eggs hatch inside the fly host and gradually devour the entire fly from the inside out.

Human use of parasitoid wasps or other natural enemies to control pest populations is known as biological control, or biocontrol. It was so successful in Hawaii that several species of parasitoid wasp have established wild populations on the islands. They have helped continuously suppress multiple fruit fly pests to this day.

The release of nonnative insects for biocontrol could have unforeseen negative consequences for local ecosystems. Therefore, federal agencies like the U.S. Department of Agriculture have strict regulations for new and existing biocontrol programs.

The enemy of my enemy is my friend

So, how do wasps achieve the impressive feat of reducing fruit fly pest populations? Once laid inside a fly host, the wasp must face the fly’s immune system, which will try to suffocate the egg before it hatches.

This inhospitable environment has forced wasps to evolve an arsenal of microscopic substances, also known as molecular factors, to combat fly defenses. These include a cocktail of different molecules introduced by the wasp mother at the time of egg-laying.

The goal of these factors is to manipulate the fruit fly’s physiological processes, like its development from egg to adult and its immune response to invading parasites. By interacting with molecular components, like proteins, that make up insect physiological pathways, parasitoid wasp factors can delay insect host development and suppress host immunity to allow the wasp offspring to feed on fly tissue unharmed.

This is the origin story of an unlikely partnership that many species of parasitoid wasp have formed with beneficial viruses. Virus particles multiply to massive quantities within the reproductive organs of female wasps during their development. Wasp mothers then use their ovipositor like a hypodermic needle to inject virus particles into host insects during egg-laying.

The virus particles turn into biological weapons that infect cells of the wasp’s host. This infection disrupts processes like the fly’s immune response. Developing wasps benefit from the virus’s activity and return the favor by passing on the virus to future wasp generations.

Not all heroes wear capes

Diachasmimorpha longicaudata is a small, bright orange wasp with a distinctively long ovipositor. The literal translation of longicaudata is “long-tailed” in Latin. But don’t let its charismatic appearance fool you.

D. longicaudata is ferocious in its ability to feast on several species of fruit fly pests, such as the Mediterranean fruit fly, Ceratitis capitata, and the oriental fruit fly, Bactrocera dorsalis. Because of D. longicaudata’s ability to attack a wide variety of fruit fly pests, pest management specialists around the world have released the wasps into agricultural ecosystems, where they dependably establish new populations and provide sustained pest control.

Like many parasitoids, D. longicaudata has formed an alliance with a virus known as Diachasmimorpha longicaudata entomopoxvirus, or DlEPV.

DlEPV replicates within the venom gland of female wasps, which stores billions of virus particles. Virus particles are so densely packed in there that they often cause the venom gland to appear iridescent blue.

DlEPV particles are highly lethal when injected into flies in the lab. The virus freezes the fly’s development and replicates with abandon until the fly’s ultimate demise.

In contrast, the alliance between wasp and virus is so strong that curing D. longicaudata wasps of their resident DlEPV infection causes the wasp offspring to die inside the fly hosts.

A new potential path forward

My colleagues and I published a study showing that DlEPV may play a critical role in helping D. longicaudata make a meal out of so many different fruit fly pests. We found a link between D. longicaudata survival and DlEPV lethality within different fruit fly host species.

When we infected C. capitata and B. dorsalis flies with DlEPV, the virus successfully replicated and killed large swaths of fly hosts. However, DlEPV couldn’t replicate within the melon fly, Zeugodacus cucurbitae, a fly species that D. longicaudata wasps cannot use as hosts.

These findings shine new light on the effect viruses have on host-parasite rivalries. The presence of these viruses could influence how useful parasitoid wasps are in getting rid of fruit fly pests. In the case of D. longicaudata, its associated virus may be responsible for the decades of reliable aid this wasp has provided to fruit fly biocontrol programs around the world.

This work has also revealed a new potential tool in the war against fruit fly pests. DlEPV is now known as a lethal enemy for several of the world’s most destructive pest species. If researchers can determine precisely how DlEPV exploits fly hosts at a molecular level, they could one day incorporate the same strategies that this virus uses into new fruit fly pest control methods.

Kelsey CoffmanUniversity of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Abdullahi Tunde Aborode, Mississippi State University

Antibiotic resistance is a growing public health problem around the world. When bacteria like E. coli no longer respond to antibiotics, infections become harder to treat.

To develop new antibiotics, researchers typically identify the genes that make bacteria resistant. Through laboratory experiments, they observe how bacteria respond to different antibiotics and look for mutations in the genetic makeup of resistant strains that allow them to survive.

While effective, this method can be time-consuming and may not always capture the full picture of how bacteria become resistant. For example, changes in how genes work that don’t involve mutations can still influence resistance. Bacteria can also exchange resistance genes between each other, which may not be detected if only focusing on mutations within a single strain.

My colleagues and I developed a new approach to identify E. coli resistance genes by computer modeling, allowing us to design new compounds that can block these genes and make existing treatments more effective.

Identifying resistance

To predict which genes contribute to resistance, we analyzed the genomes of various E. coli strains to identify genetic patterns and markers associated with resistance. We then used machine learning algorithms trained on existing data to highlight novel genes or mutations shared across resistant strains that might contribute to resistance.

After identifying resistance genes, we designed inhibitors that specifically target and block the proteins these genes produce. By analyzing the structure of the proteins these genes code for, we were able to optimize our inhibitors to strongly bind to these specific proteins.

To reduce the likelihood that bacteria would evolve resistance to these inhibitors, we targeted regions of their genome that code for proteins critical to their survival. By interfering with how bacteria carry out important functions, it makes it more difficult for them to develop mechanisms to compensate. We also prioritized compounds that work differently from existing antibiotics to minimize cross-resistance.

Finally, we tested how effectively our inhibitors could overcome antibiotic resistance in E. coli. We used computer simulations to assess how strongly a number of inhibitors bind to target proteins over time. One inhibitor called hesperidin was able to strongly bind to the three genes in E. coli involved in resistance that we identified, suggesting it may be able to help combat antibiotic-resistant strains.

A global threat

The World Health Organization ranks antimicrobial resistance as one of the top 10 threats to global health. In 2019, bacterial antibiotic resistance killed an estimated 4.95 million people worldwide.

By targeting the specific genes responsible for resistance to existing drugs, our approach could lead to treatments for challenging bacterial infections that are not only more effective but also less likely to contribute to further resistance. It can also help researchers keep up with bacterial threats as they evolve.

Our predictive approach could be adapted to other bacterial strains, allowing for more personalized treatment strategies. In the future, doctors could potentially tailor antibiotic treatments based on the specific genetic makeup of the bacteria causing the infection, potentially leading to better outcomes.

As antibiotic resistance continues to rise globally, our findings may provide a crucial tool in the fight against this threat. Further development is needed before our methods can be used in the clinic. But by staying ahead of bacterial evolution, targeted inhibitors could help preserve the efficacy of existing antibiotics and reduce the spread of resistant strains.

Abdullahi Tunde Aborode, , Mississippi State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

David Markowitz, Michigan State University

Artificial intelligence-generated summaries of scientific papers make complex information more understandable for the public compared with human-written summaries, according to my recent paper published in PNAS Nexus. AI-generated summaries not only improved public comprehension of science but also enhanced how people perceived scientists.

I used a popular large language model, GPT-4 by OpenAI, to create simple summaries of scientific papers; this kind of text is often called a significance statement. The AI-generated summaries used simpler language – they were easier to read according to a readability index and used more common words, like “job” instead of “occupation” – than summaries written by the researchers who had done the work.

In one experiment, I found that readers of the AI-generated statements had a better understanding of the science, and they provided more detailed, accurate summaries of the content than readers of the human-written statements.

I also investigated what effects the simpler summaries might have on people’s perceptions of the scientists who performed the research. In this experiment, participants rated the scientists whose work was described in the simpler texts as more credible and trustworthy than the scientists whose work was described in the more complex texts.

In both experiments, participants did not know who wrote each summary. The simpler texts were always AI-generated, and the complex texts were always human-generated. When I asked participants who they believed wrote each summary, they ironically thought the more complex ones were written by AI and simpler ones were written by humans.

Why it matters

Have you ever read about a scientific discovery and felt like it was written in a foreign language? If you’re like most Americans, new scientific information is probably hard to understand – especially if you try to tackle a science article in a research journal.

In an era where scientific literacy is crucial for informed decision-making, the abilities to communicate and grasp complex ideas are more important than ever. Trust in science has been declining for years, and one contributing factor may be the challenge of understanding scientific jargon.

This research points to a potential solution: using AI to simplify science communication. By making scientific content more approachable, this work demonstrates that AI-generated summaries may help to restore trust in scientists and, in turn, encourage greater public engagement with scientific issues. The question of trust is particularly important, as people often rely on science in their daily lives, from eating habits to medical choices.

What still isn’t known

As AI continues to evolve, its role in science communication may expand, especially if using generative AI becomes more commonplace or sanctioned by journals. Indeed, the academic publishing field is still establishing norms regarding the use of AI. By simplifying scientific writing, AI could contribute to more engagement with complex issues.

While the benefits of AI-generated science communication are perhaps clear, ethical considerations must also be considered. There is some risk that relying on AI to simplify scientific content may remove nuance, potentially leading to misunderstandings or oversimplifications. There’s always the chance of errors, too, if no one pays close attention.

Additionally, transparency is critical. Readers should be informed when AI is used to generate summaries to avoid potential biases.

Simple science descriptions are preferable to and more beneficial than complex ones, and AI tools can help. But scientists could also achieve the same goals by working harder to minimize jargon and communicate clearly – no AI necessary.

David Markowitz, Associate Professor of Communication, Michigan State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.