Researchers led by Sa Kan Yoo at the RIKEN Center for Biosystems Dynamics Research (BDR) in Japan have discovered that the natural turnover of gut cells in fruit flies depends on how much food the flies get, rather than on any specific nutrient. They found that gut cells were replaced more frequently when nutrient levels were low and the insides of gut cells were fluid and watery.

Researchers led by Sa Kan Yoo at the RIKEN Center for Biosystems Dynamics Research (BDR) in Japan have discovered that the natural turnover of gut cells in fruit flies depends on how much food the flies get, rather than on any specific nutrient. They found that gut cells were replaced more frequently when nutrient levels were low and the insides of gut cells were fluid and watery.

Scientists trained artificial intelligence on libraries of DNA and then asked the model to create recipes for viral genomes. Sixteen of them were viable, yielding new viruses.

Researchers say breakthrough offers hope for new medicines but also raises urgent biosecurity questions

Scientists have made the first viruses designed by artificial intelligence in a milestone that raises hopes for new medicines but also concerns over how to ensure the technology remains safe.

The viruses are specific kinds known as bacteriophages, which only infect bacteria and are used around the world to treat patients with persistent infections. In lab tests, a cocktail of the AI-designed viruses killed E coli bugs that were resistant to natural bacteriophages.

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© Photograph: Samyukta Lakshmi/Bloomberg/Getty Images

© Photograph: Samyukta Lakshmi/Bloomberg/Getty Images

© Photograph: Samyukta Lakshmi/Bloomberg/Getty Images

The sheer amount of research that has gone into trying to find alternative explanations to what Behe has proposed is mind-boggling.

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Malaria parasites proliferate in an unusual way. Rather than dividing into two daughter cells like human cells, they first amplify their genetic material tenfold, hundredfold or even thousandfold before simultaneously producing a corresponding number of daughter parasites. Until now, the mechanisms controlling these processes were only partly understood.

Xenophagocytosis is a natural immune process that limits interspecies organ generation by eliminating living donor cells, researchers at the Institute of Science Tokyo, Japan, report. By uncovering how embryonic macrophages eliminate living donor cells, the researchers developed strategies to block this response. Using this strategy, they significantly improved donor cell survival and the generation of rat pancreas in mice, bringing us one step closer to producing transplantable human organs in animals.

Every time a cell divides, it must accurately copy and distribute its DNA between two new cells. However, in some cases, chromosomes do not fully separate, leaving behind thin strands of DNA known as DNA bridges that connect the two newly formed cells.

The fungal puppet master has been living a secret life

The post The Bizarre Life Cycle of “Zombie-Ant” Fungus Gets a New Twist appeared first on Nautilus.

Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.

One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.

Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.

To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.

​The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes. 

They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios. 

After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR). 

They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model. 

They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.

Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions. 

The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.

In a recent study, scientists devised a way of reversing the buildup of compounds that lead to some age-related diseases.

La Trobe University scientists have uncovered the critical role fatty cell structures play in our body's early detection and defense against viruses. Published in Nature Communications, the discovery could open the door to a new type of antiviral strategy that harnesses the body's innate defense mechanisms rather than targeting individual viruses.

Cells are like metropolises, home to millions of molecular residents. If one were to stand atop a high-rise, trying to identify most of its inhabitants would seem an impossible task. Even with the sophisticated imaging tools currently available to scientists, it is challenging to zoom in on specific molecules and view them with a high degree of detail.

It hijacks a common process present in all cells

The post How This Emerging Tick-borne Virus Escapes Your Immune System appeared first on Nautilus.

Another last-resort antibiotic has fallen victim to the rapid evolution of drug-resistant superbugs. The powerful antibiotic combination ceftazidime-avibactam (CZA), widely used to treat severe hospital-acquired infections caused by Pseudomonas aeruginosa—particularly in critically ill and immunocompromised patients—can no longer eliminate some of these infections.

Researchers at Queen Mary University of London have discovered that tiny photosynthetic bacteria band together into protective "herds" when attacked by predators—a survival strategy that could also influence how carbon is stored in the world's waters.

The nucleolus is a liquid-like cellular organelle where protein factories called ribosomes are assembled. Researchers knew of three distinct compartments within the nucleolus, but how these compartments function to drive ribosome assembly was unclear. A study from St. Jude Children's Research Hospital, published today in Molecular Cell, reveals that smaller subcompartments containing ribosome building blocks and assembly proteins spontaneously form to finish the final steps of ribosome assembly. This reveals unprecedented levels of organization behind the process and may offer insight into diseases associated with increased ribosome production, such as cancer.

Nitrogen is an essential building block of proteins, amino acids, and DNA. Without nitrogen, cells can’t function or reproduce. Nitrogen is abundant in the atmosphere as dinitrogen gas, but plants and animals can’t access atmospheric nitrogen. Instead, we rely on biologically available forms like ammonia and nitrate. 

Specialized microbes can transform atmospheric nitrogen into ammonia via a process called biological nitrogen fixation. Nitrogen-fixing soil bacteria have a unique protein complex, the nitrogenase complex, that facilitates this nitrogen transformation. The nitrogenase complex is activated by a group of genes known as nif genes. 

Legume plants like peanuts, peas, and beans have nitrogen-fixing bacteria in the soil zone around their roots, called the rhizosphere. The symbiotic relationship between nitrogen-fixing soil bacteria and legume plants supplies most of the biologically available nitrogen to life on Earth.

However, one piece of the puzzle remains unexplored: the role of soil viruses in these nitrogen transformations, referred to as the nitrogen cycle. Viruses are microscopic particles made up of genetic material and proteins, but they can’t reproduce without a host cell. Scientists consider viruses to be biological entities that are halfway between living and non-living.

To reproduce, viruses contact a host cell and inject their genetic material into it. The virus’s genetic material takes over the host cell’s genome and forces the cell to produce proteins that assemble into new viruses. As new viruses are created, they can incorporate genes from the host cell into their own genetic material. Genes that are transferred from a host cell to a virus are known as auxiliary metabolic genes (AMGs). Viruses can carry AMGs and pass them to other hosts or use them to increase their own ecological fitness. 

Scientists from China, Spain, and the Czech Republic set out to investigate whether viruses from legume roots could increase local soil nitrogen fixation by expressing nif AMGs or by transferring nif genes to other bacteria. First, they determined the global distribution of viral nitrogen-fixing genes by analyzing about 8.6 million viral genomes from the Integrated Microbial Genomes/Virus database. They looked for the DNA sequences of known nitrogen-fixing genes and the locations where the viruses were sampled. They found that only 0.003% of the viruses in the database had at least 1 nitrogen-fixing gene, but they clustered in the same locations as nitrogen-fixing bacteria. 

The team also found 3 types of viruses that most commonly carried nitrogen-fixing genes: Kyanoviridae, Nudiviridae, and Bronfenbrennervirinae. Kyanoviridae viruses carried a nif gene called nifU. The researchers used a computer program called DRAM-v to confirm that nifU was a fully functional gene and a real tool that viruses could use. 

Next, the team collected soil samples from a cowpea field in Nanjing, China, to test whether the cowpea roots influenced the abundance of viruses using nitrogen-fixing genes. They collected soil from the cowpea rhizospheres and soil away from the plants. They analyzed both viral and bacterial RNA in the soil samples to identify active genes, using a sequencing process called metatranscriptomics. They found that the viral nifU gene was actively expressed in the cowpea rhizosphere samples more than in the non-crop soil. Although about 96% of nifU genes were being expressed by bacteria, viruses contributed about 4%. 

The researchers then assessed whether rhizospheric viruses changed the amount of nitrogen being fixed in these soils. They set up small, enclosed containers of sterile soil and added either bacteria or a mixture of bacteria and viruses extracted from the cowpea rhizospheres. They found that soil with viruses had higher total nitrogen (36 milligrams per kilogram, or mg/kg) than soil without viruses (17 mg/kg). 

Finally, they adjusted the air in each container to have a different form of dinitrogen gas. Atoms of the same element that have different atomic masses are known as isotopes. Nitrogen has 2 stable isotopes: lighter nitrogen-14 and heavier nitrogen-15. Bacteria or viruses that fix nitrogen at natural isotope abundances will take up nitrogen-14. But scientists can track who in the population fixes nitrogen by adding nitrogen-15 to the air, a process called nitrogen-15 stable isotope probing. Nitrogen-fixers will incorporate the heavy nitrogen-15 into their biomass over time and increase their weight, allowing researchers to separate and identify them.

The team grew the bacteria and viruses with nitrogen-15 for 35 days, then used stable isotope probing to separate the nitrogen fixers. They detected viral nifU AMGs among the heavier biomass, confirming that rhizospheric viruses were fixing nitrogen. 

The team concluded that rhizospheric viruses and the viral nifU AMG can increase nitrogen fixation in cowpea soils. The team suggested that although viruses with nitrogen-fixing genes are rare, they can influence the soil nitrogen cycle. They recommended that future researchers further test the contribution of viral nifU genes to plant nitrogen fixation with controlled infection experiments.

The post Viruses help plants obtain nitrogen appeared first on Sciworthy.

They could complicate the hunt for extraterrestrial life—and compromise astronaut health

The post All the Microbes That Could Survive in Space appeared first on Nautilus.

Scientists have uncovered a shared target used by multiple diarrhea-causing bacteria to invade the gut. Despite decades of research, scientists still do not have vaccines against two of the world’s most common causes of severe bacterial diarrhea: enterotoxigenic E. coli (ETEC) and Shigella. Together, these pathogens infect hundreds of millions of people each year and [...]