Baker's yeast is one of biotechnology's most important microorganisms. It has long been used to leaven bread and ferment beer. More recently, advances in synthetic biology have enabled engineered yeast to convert sugar into a wide range of products, including medicines, fuels and industrial chemicals. Yet even well-engineered yeast cells can be difficult to control in a precise and timely way.

Baker's yeast is one of biotechnology's most important microorganisms. It has long been used to leaven bread and ferment beer. More recently, advances in synthetic biology have enabled engineered yeast to convert sugar into a wide range of products, including medicines, fuels and industrial chemicals. Yet even well-engineered yeast cells can be difficult to control in a precise and timely way.

Every living cell, from the simplest bacterium to a human neuron, relies on the same basic machinery to read, interpret, and use its DNA. This molecular machinery is so fundamental that life as we know it cannot exist without it. And because it is so essential, it has long been a target for drugs; it shuts down a bacterium's ability to access its own genes and it quickly dies.

The global expansion of chicken farming has created conditions that allow Campylobacter to spread, mix and adapt more readily. Commercial poultry farms now hold billions of chickens, creating a vast habitat where bacteria can move, mix and adapt. An analysis from the Ineos Oxford Institute for Antimicrobial Research at the University of Oxford found that [...]

Photosynthetic microorganisms can use light energy to convert carbon dioxide into useful compounds under mild, environmentally compatible conditions. However, many current photosynthetic production systems rely on cells growing freely in large volumes of liquid. This creates a substantial demand for water and requires extensive mixing to keep the cells evenly distributed and exposed to light, increasing the resource and energy requirements of the process.

For most healthy people, Cryptococcus is an unnoticed infection. The immune system walls it off in the lungs and keeps it dormant for life. But in patients undergoing cancer chemotherapy, recovering from an organ transplant, living with HIV or taking newer immunosuppressing drugs for autoimmune disease, the fungus can reactivate, spread to the brain and cause meningitis.

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.