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Endosymbiotic theory suggests that eukaryotic cells derived from the process of symbiosis. Larger prokaryotic cells engulfed smaller ones forming a symbiotic relationship, which over time give rise to organelles like the mitochondrion, and chloroplasts. Mitochondria allowed eukaryotes to produce energy more efficiently, and chloroplasts allowed photosynthesis. This symbiotic event not only provided a major energy advantage but also laid the foundation for the diversity and complexity of modern life, including the evolution of multicellular organisms. Understanding the origin of eukaryotes will bring us one step closer to answering one of the central questions in biology: How life was created on Earth.

Donoghue PCJ, Kay C, Spang A, et al. Defining eukaryotes to dissect eukaryogenesis. Current Biology. 2023;33(17):R919-R929. doi:10.1016/j.cub.2023.07.048

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In the 1930s, intensive mono-cropping in the US lead to an event known as the "Dust Bowl" with disastrous economic and social consequences. Without the tools and resources needed to facilitate sustainable agriculture, similar events are likely to occur again in vulnerable countries across the Global South.

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When the southern Great Plains of the US were blighted with a series of droughts in the 1930s, it had an unparalled impact on the whole country. Combined with decades of ill-advised farming policy, the result was the Dust Bowl. Massive dust storms began in 1931 and devastated the country's major cereal producing areas. US wheat and maize production crashed by 32% in 1933 and continued to fall for the rest of the decade as more droughts hit.

By 1934, 14 million hectares of agricultural land was degraded beyond use, while a further 51 million hectares (roughly three-quarters the size of Texas) was rapidly shedding its topsoil. Millions of people lost their livelihoods. The desperate migration that followed was immortalised in John Steinbeck's novel The Grapes of Wrath.

But what consequences would a disruption like the Dust Bowl have now, when the Great Plains of the US are not just the breadbasket of America, but a major producer of staple cereals that are exported around the world? As part of an international team of researchers, we ran a computer simulation to find out.

More eggs in fewer baskets

Today, the global food system is more connected than ever. Major disruptions to production in one region, like the Dust Bowl caused, could have a ripple effect on global food supply and prices.

Food trade has been increasing rapidly since the mid 1900s, and 80% of the world population now lives in countries that import more food calories than they export. For roughly half of us, dependence on imported calories and protein has increased during the past three decades, while almost two thirds of people increasingly rely on imported fruits and vegetables for essential micronutrients.

Many countries, ranging from relatively small nations like Finland to highly populous China and India, are increasing their reliance on imports while reducing the number of trade links, essentially putting more of their eggs in fewer baskets. At the same time, a few countries are becoming hubs of global food production, such as the US and Brazil who dominate exports of soybean, which is used primarily as animal feed.

Cascading shocks

According to the recent simulation, a decline in US wheat production of the same magnitude as occurred during the Dust Bowl (about 30% over four consecutive years), would deplete nearly all wheat reserves in the US and reduce global stocks by 31%. Since the US is one of the world's largest exporters of wheat and has many trade links, nearly all countries would be affected.

Lower wheat reserves could cause a shortage of products like flour, pasta and bread, making them too expensive for many to buy, especially in poorer countries. Even if a country doesn't trade wheat with the US directly, the cascading effects of the production shock could be felt through other trading partners. Countries seeking to meet their needs with limited supply from the US would need to increase imports from elsewhere and decrease their exports, passing on the disruption to other trade partners.

As global food reserves shrink, it leaves the world even more exposed to future shocks. Without this buffer, wheat products are likely to be rationed, directly raising global food prices.

The dust bowl simulation illustrates how trade can transmit the consequences of production shocks in one part of the world to countries far away. But global trade is a double-edged sword. It can help overcome temporary shortages in local supply and enable a rich and nutritious diet. Globalisation has moved food production to regions where it's more efficient - whether in terms of economic cost or resources like land and water. This has helped save cropland and water and allowed populations to prosper even where local resources are scarce.

Building resilience

The COVID-19 pandemic has already led to some countries restricting food exports, with the potential for shortages. But the risks of climate change causing shocks in food production are looming too.

The warming climate intensifies extreme weather such as droughts, floods and storms, and increases the risk of simultaneous crop failures around the world. At the start of 2020, unusually wet weather helped breed Kenya's worst locust outbreak for more than 70 years, which has the potential to consume vast acres of crops.

But even with so much uncertainty and risk, it's hard to imagine people giving up the benefits of a global food system. Would any of us really want to go back to a time when we couldn't enjoy food from distant places and different climates at any time of year?

Read more: Our climate is like reckless banking before the crash - it's time to talk about near-term collapse

But perhaps we should question the desire for efficiency that has driven the current system and instead aim to build one that can withstand shocks.

Small-scale farmers plant several different crops to ensure the failure of one isn't a catastrophe. The same principle can be applied on a much larger scale to the global food system. Procuring a diverse range of staple foods and sources for growing them can help to ensure that the failure of one component - whether it's one protein source or one trading partner growing it - can be compensated by another.

The modern dust bowl simulation can help to illuminate some of the systemic risks in the global food system, but the COVID-19 pandemic is a better demonstration of how fragile our hyperconnected world is. Rather than try to revert to the way things were before the crisis, countries should seize the opportunity to transform this system to something more resilient, so that when the next major disruption hits, we'll be prepared.

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We simulated how a modern dust bowl would impact global food supplies and the result is devastating

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When the southern Great Plains of the US were blighted with a series of droughts in the 1930s, it had an unparalled impact on the whole country. Combined with decades of ill-advised farming policy, the result was the Dust Bowl. Massive dust storms began in 1931 and devastated the country's major cereal producing areas.

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Food for thought: nutrition and women's health

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Food for thought: nutrition and women's health

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Patients of under-researched health issues such as PCOS and endometriosis are turning to inflammatory diets to help manage their symptoms, but with the prevalence of inaccurate health advice on social media (like this recent example https://www.bbc.co.uk/news/articles/ckgz2p0999yo, or the story that inspired the Netflix mini-series "Apple Cider Vinegar") there may be a need for more caution and nuance surrounding the topic.

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ByJacqui WakefieldBBC 100 Women and Global Disinformation Unit, BBC World Service

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Influencers selling fake cures for polycystic ovary syndrome

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Influencers posing as medical experts are exploiting the lack of easy solutions for PCOS by selling fake cures.

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Is it mostly genetics or environmental factors, or a mix of both? Some allergies seem to be able to be cured (like this intriguing therapy for peanut allergies https://www.newscientist.com/article/2402095-peanut-toothpaste-shows-promise-at-preventing-allergic-reactions/), while others develop or get worse later in life - what are the mechanisms behind this?

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Peanut toothpaste shows promise at preventing allergic reactions

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A toothpaste that contains peanut proteins did not cause any serious side effects in people with an allergy to the food and showed early signs of preventing dangerous reactions

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Something to chew on: the journey to lab-grown teeth

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Something to chew on: the journey to lab-grown teeth

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Following a long line of developments in the field of regenerative dentistry over the past 20 years, researchers have now succeeded in growing human teeth in the lab (https://www.bbc.co.uk/news/articles/cj9ejj3jzrwo). This exciting breakthrough promises to revolutionise the lives of patients suffering from poor oral health, which is linked to an increased risk of heart disease.

Sources:

Young, C. S. et al. Tissue engineering of complex tooth structures on biodegradable polymer scaffolds. Journal of Dental Research 81, 695–700 (2002).

Young, E. & Khamsi, R. Lab-grown replacement teeth fill the gap. New Scientist (2007).

Beresford, P. & Gidley, S. New breakthrough in lab grown teeth. BDJ 206, 246 (2009).

Krivanek, J. et al. Dental cell type atlas reveals stem and differentiated cell types in mouse and human teeth. Nature Communications 11, (2020).

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King’s College scientists’ lab-grown human teeth breakthrough

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Scientists from King’s College London manage to grow a human tooth under laboratory conditions.

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As antimicrobial resistance and densely packed urban populations continue to rise across the globe, the potential spread of lethal infections poses a significant threat to the future of public health. In search of solutions, scientists are studying the infection control strategies of another species which has evolved to survive in highly-populated colonies: ants. Antimicrobial peptides isolated from the venom of ants in India have shown potential for the development of novel antibiotics. Another study on antimicrobial peptides derived from the venom of ants in Brazil has suggested they might also have an application in the treatment of epilepsy, due to their modulation of ion channels and neurone receptors.

Sources:

Aravinda, H. et al. Isolation of antimicrobial peptides from seed harvester ant, Trichomyrmex scabriceps (Mayr) (Hymenoptera: Formicidae) and their antimicrobial assay. Arabian Journal of Chemistry 16, 105162 (2023).

Paes, L. C. F. et al. Exploring the neuroprotective potential of antimicrobial peptides from Dinoponera quadriceps venom against pentylenetetrazole-induced seizures in vivo. Toxicon 237, 107538 (2023).

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"Deep learning approaches have aided in exploring chemical spaces; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics."

(Taken from the abstract of: Wong, F. et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 626, 177–185 (2023).)

https://news.mit.edu/2023/using-ai-mit-researchers-identify-antibiotic-candidates-1220

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MIT News | Massachusetts Institute of Technology

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Using a type of artificial intelligence known as deep learning, MIT researchers have discovered a class of compounds that can kill a drug-resistant bacterium that causes more than 10,000 deaths in the United States every year.

In a study appearing today in Nature, the researchers showed that these compounds could kill methicillin-resistant Staphylococcus aureus (MRSA) grown in a lab dish and in two mouse models of MRSA infection. The compounds also show very low toxicity against human cells, making them particularly good drug candidates.

A key innovation of the new study is that the researchers were also able to figure out what kinds of information the deep-learning model was using to make its antibiotic potency predictions. This knowledge could help researchers to design additional drugs that might work even better than the ones identified by the model.

"The insight here was that we could see what was being learned by the models to make their predictions that certain molecules would make for good antibiotics. Our work provides a framework that is time-efficient, resource-efficient, and mechanistically insightful, from a chemical-structure standpoint, in ways that we haven't had to date," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

Felix Wong, a postdoc at IMES and the Broad Institute of MIT and Harvard, and Erica Zheng, a former Harvard Medical School graduate student who was advised by Collins, are the lead authors of the study, which is part of the Antibiotics-AI Project at MIT. The mission of this project, led by Collins, is to discover new classes of antibiotics against seven types of deadly bacteria, over seven years.
Explainable predictions
MRSA, which infects more than 80,000 people in the United States every year, often causes skin infections or pneumonia. Severe cases can lead to sepsis, a potentially fatal bloodstream infection.

Over the past several years, Collins and his colleagues in MIT's Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) have begun using deep learning to try to find new antibiotics. Their work has yielded potential drugs against Acinetobacter baumannii, a bacterium that is often found in hospitals, and many other drug-resistant bacteria.

These compounds were identified using deep learning models that can learn to identify chemical structures that are associated with antimicrobial activity. These models then sift through millions of other compounds, generating predictions of which ones may have strong antimicrobial activity.

These types of searches have proven fruitful, but one limitation to this approach is that the models are "black boxes," meaning that there is no way of knowing what features the model based its predictions on. If scientists knew how the models were making their predictions, it could be easier for them to identify or design additional antibiotics.

"What we set out to do in this study was to open the black box," Wong says. "These models consist of very large numbers of calculations that mimic neural connections, and no one really knows what's going on underneath the hood."

First, the researchers trained a deep learning model using substantially expanded datasets. They generated this training data by testing about 39,000 compounds for antibiotic activity against MRSA, and then fed this data, plus information on the chemical structures of the compounds, into the model.

"You can represent basically any molecule as a chemical structure, and also you tell the model if that chemical structure is antibacterial or not," Wong says. "The model is trained on many examples like this. If you then give it any new molecule, a new arrangement of atoms and bonds, it can tell you a probability that that compound is predicted to be antibacterial."

To figure out how the model was making its predictions, the researchers adapted an algorithm known as Monte Carlo tree search, which has been used to help make other deep learning models, such as AlphaGo, more explainable. This search algorithm allows the model to generate not only an estimate of each molecule's antimicrobial activity, but also a prediction for which substructures of the molecule likely account for that activity.
Potent activity
To further narrow down the pool of candidate drugs, the researchers trained three additional deep learning models to predict whether the compounds were toxic to three different types of human cells. By combining this information with the predictions of antimicrobial activity, the researchers discovered compounds that could kill microbes while having minimal adverse effects on the human body.

Using this collection of models, the researchers screened about 12 million compounds, all of which are commercially available. From this collection, the models identified compounds from five different classes, based on chemical substructures within the molecules, that were predicted to be active against MRSA.

The researchers purchased about 280 compounds and tested them against MRSA grown in a lab dish, allowing them to identify two, from the same class, that appeared to be very promising antibiotic candidates. In tests in two mouse models, one of MRSA skin infection and one of MRSA systemic infection, each of those compounds reduced the MRSA population by a factor of 10.

Experiments revealed that the compounds appear to kill bacteria by disrupting their ability to maintain an electrochemical gradient across their cell membranes. This gradient is needed for many critical cell functions, including the ability to produce ATP (molecules that cells use to store energy). An antibiotic candidate that Collins' lab discovered in 2020, halicin, appears to work by a similar mechanism but is specific to Gram-negative bacteria (bacteria with thin cell walls). MRSA is a Gram-positive bacterium, with thicker cell walls.

"We have pretty strong evidence that this new structural class is active against Gram-positive pathogens by selectively dissipating the proton motive force in bacteria," Wong says. "The molecules are attacking bacterial cell membranes selectively, in a way that does not incur substantial damage in human cell membranes. Our substantially augmented deep learning approach allowed us to predict this new structural class of antibiotics and enabled the finding that it is not toxic against human cells."

The researchers have shared their findings with Phare Bio, a nonprofit started by Collins and others as part of the Antibiotics-AI Project. The nonprofit now plans to do more detailed analysis of the chemical properties and potential clinical use of these compounds. Meanwhile, Collins' lab is working on designing additional drug candidates based on the findings of the new study, as well as using the models to seek compounds that can kill other types of bacteria.

"We are already leveraging similar approaches based on chemical substructures to design compounds de novo, and of course, we can readily adopt this approach out of the box to discover new classes of antibiotics against different pathogens," Wong says.

In addition to MIT, Harvard, and the Broad Institute, the paper's contributing institutions are Integrated Biosciences, Inc., the Wyss Institute for Biologically Inspired Engineering, and the Leibniz Institute of Polymer Research in Dresden, Germany. The research was funded by the James S. McDonnell Foundation, the U.S. National Institute of Allergy and Infectious Diseases, the Swiss National Science Foundation, the Banting Fellowships Program, the Volkswagen Foundation, the Defense Threat Reduction Agency, the U.S. National Institutes of Health, and the Broad Institute. The Antibiotics-AI Project is funded by the Audacious Project, Flu Lab, the Sea Grape Foundation, the Wyss Foundation, and an anonymous donor.

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Using AI, MIT researchers identify a new class of antibiotic candidates

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Using artificial intelligence, MIT researchers discovered a class of compounds that can kill methicillin-resistant Staphylococcus aureus (MRSA), a drug-resistant bacterium that causes more than 10,000 deaths in the U.S. each year.

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Data Driven Sport: HRV and enhancing athlete recovery

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Data Driven Sport: HRV and enhancing athlete recovery

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Heart Rate Variability is a physiological statistic which is increasingly being used in performance sport to enhance recovery and maximise training outcomes. There’s an increasing amount of research and technologies relating to HRV and it’s an interesting, relevant topic which shows how physiology is applied to performance sport.

Sources:

Dong, J.-G. The role of heart rate variability in sports physiology. Experimental and Therapeutic Medicine 11, 1531–1536 (2016).

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Acupuncture is one of the most common traditional Chinese medicine treatments and is widely used in the treatment of pain. Despite its emergence in the UK between mid to late 20th century and its widespread use in China since BCE, there still isn't a concrete explanation as to how it exerts its analgesic effects. However, clinical trials and research have proposed potential mechanisms of action.

Sources:

Kawakita, K. & Okada, K. Acupuncture therapy: mechanism of action, efficacy, and safety: a potential intervention for psychogenic disorders? BioPsychoSocial Medicine 8, 4 (2014).

Zukerman, W. Why acupuncture aids spinal recovery. New Scientist (2010). https://www.newscientist.com/article/dn18817-why-acupuncture-aids-spinal-recovery/ Accessed online: 24/03/25

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Rats with damaged spines can walk again thanks to acupuncture, which seems to halt nerve death