Electric vehicles (EVs) use high-powered rechargeable batteries to store electrical energy that powers their motors. Most EV batteries use a specific mixture of metals to hold the battery’s charge, called nanoscale lithiated nickel manganese cobalt oxides, aka NMC materials. NMC materials can store a large amount of energy, making them ideal for batteries, but the metals themselves can be toxic in soil and water if the batteries are not properly recycled.  

NMC batteries leave behind a specific combination of toxic metals, so researchers can study how bacteria respond to this waste when it enters the environment. By understanding how bacteria adapt to toxic metals, scientists hope to predict how new technologies like EV batteries will affect the environment. 

In 2019, a team of researchers from the University of Minnesota reported that a common soil bacterium called Shewanella oneidensis can rapidly evolve resistance to NMC materials, but they didn’t know how. True beneficial gene evolution usually develops over thousands of generations, but Shewanella oneidensis adapted much more quickly. The team recently conducted a follow-up study to determine the genetic mechanisms behind this bacterium’s unique metal resistance.

They started by analyzing the complete DNA sequences of 33 Shewanella colonies that were resistant to either pure NMC waste or individual metals like nickel and cobalt, using a process called whole-genome sequencing. They expected to find DNA mutations in genes that control how bacteria take up and expel toxic metals that would give the resistant Shewanella an advantage in surviving NMC metal toxicity. However, they didn’t find any DNA mutations in metal-related genes. Instead, they found that a specific DNA region was duplicated 10 to 15 times in all 33 NMC-resistant or metal-resistant colonies. This result suggested to the researchers that a temporary adaptation, not true gene evolution, was responsible for the bacteria’s metal resistance. 

The researchers wanted to know more about the duplicated DNA region in metal-resistant Shewanella, so they focused on 2 gene duplication mechanisms that might serve as temporary adaptations in Shewanella. The first mechanism directly duplicates DNA regions to create extra copies of a gene, via a process called gene duplication amplification. The second mechanism moves DNA segments, called mobile genetic elements, into different DNA sections to create extra copies of a gene. 

To test whether either of these mechanisms was causing the gene duplication in Shewanella, the team grew the bacteria in different liquid media containing only cobalt, only nickel, or cobalt and nickel together. The researchers used a technique that measures the exact amount of DNA in a given sequence, called quantitative polymerase chain reaction (qPCR), to calculate how many duplicated DNA regions were in each genome sequence. They found that neither cobalt nor nickel alone triggered DNA duplication. But the bacteria grown in the media with cobalt and nickel together had 8 additional copies of the DNA region. This suggested that something about the combination of nickel and cobalt triggered gene duplication. 

To determine whether this gene duplication only occurred with nickel and cobalt, the team substituted copper and zinc for nickel in the growth media. They exposed Shewanella cells to different combinations of these metals using a grid pattern in a 96-well plate, a technique called a checkerboard assay. Then they repeated the qPCR process to measure the duplicated DNA region. They found that no other combinations of metals triggered gene duplication, suggesting that nickel and cobalt are especially toxic together, and that the gene duplication responds to this combined toxicity.

Finally, the researchers identified the function of the duplicated DNA region. They found that one of the genes within the region turned on a special protein that pushes metal ions and other toxins out of the cell, called an efflux pump protein. The team hypothesized that this efflux pump protein was responsible for removing cobalt and nickel from the bacteria’s cells. To test this theory, they grew modified Shewanella cells without the efflux pump gene in media containing cobalt, nickel, or cobalt and nickel together. They found that the modified bacteria grew well in the nickel-only treatment, but didn’t grow in the cobalt-only or combined treatments, which indicated that the efflux pump protein is necessary for Shewanella to resist cobalt toxicity. 

Based on their experiments, the team concluded that nickel and cobalt together trigger a gene duplication event in Shewanella oneidensis. This gene duplication event increases the number of efflux proteins in Shewanella that can expel cobalt and underlies its unique NMC resistance. They suggested that future researchers study temporary adaptations similar to gene duplication in bacteria to track how they respond to electronic waste and other new technologies.

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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. 

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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.

Bacteria attached to plant roots “feed” nitrogen to plants through the soil, and in turn receive organic acids for energy from plants performing photosynthesis. Source: Wikimedia Commons

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.

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Brown rot is a common fungal disease in the Southeastern United States that affects stone fruits like peaches, cherries, and plums. Monilinia fructicola is a tan, fuzzy fungus that grows on tree flowers, twigs, and fruit, and is the most common cause of brown rot. Once brown rot infects a tree, the damage is irreversible, causing significant crop losses in commercial orchards. Farmers try to control the spread of M. fructicola by applying chemical sprays, called fungicides, to fruit trees in the spring.

In M. fructicola, a biosynthesis gene named MfCYP51 encodes a major protein critical to the fungi’s survival. Certain fungicides, called demethylation inhibitor (DMI) fungicides, stop M. fructicola from growing by targeting this protein. Over the last 20 years, DMI fungicides have become less effective against brown rot as M. fructicola becomes more resistant. 

Living microorganisms and their metabolites can control fungal diseases as an alternative to chemical fungicides, known as biofungicides. Many bacteria produce compounds like hydrogen cyanide and pyrrolnitrin that damage fungal cells. However, scientists have not found a biofungicide suitable for commercial production.​ A group of researchers at Clemson University in the USA and Huazhong Agricultural University in China set out to test whether the soil bacteria Pseudomonas chlororaphis and Bacillus subtilis could be effective biofungicides against brown rot. 

The researchers performed an incubation experiment to analyze how the fungal cells make their critical protein from the MfCYP51 gene, a process called gene expression, under different fungicide treatments. They used 3 M. fructicola strains sensitive to traditional DMI fungicides and 3 resistant strains. They tested these with 5 different fungicide treatments: a DMI fungicide, a mixture of P. chlororaphis metabolites, a mixture of living B. subtilis cells, a DMI fungicide + P. chlororaphis metabolites, and a DMI fungicide +  B. subtilis cells. They also set up a control treatment with sterile water instead of fungicide.  

After 6 hours, the researchers extracted the molecule that measures gene expression, called RNA, from the fungi. They found that both the P. chlororaphis treatment and the DMI fungicide + P. chlororaphis treatment substantially reduced MfCYP51 expression in both the sensitive and resistant isolates compared to the controls. In contrast, the DMI fungicide, B. subtilis, and DMI fungicide + B. subtilis treatments increased MfCYP51 expression in the resistant isolates. The researchers concluded that P. chlororaphis has a unique ability to reduce gene expression, unlike other biofungicides.

​To further investigate how the biofungicide treatments work, the team tested whether P. chlororaphis and B. subtilis produced the anti-fungal metabolite pyrrolnitrin. They added 1 milliliter of each treatment to a machine that separates and identifies liquid compounds using high pressure, called a high-performance liquid chromatograph. They found that the P. chlororaphis treatment contained pyrrolnitrin, so they hypothesized that it was responsible for reducing gene expression.

Additionally, the researchers tested their 5 treatments on brown rot in fruit. For each treatment, they took 10 Gala apples, washed and sterilized their surfaces, and sprayed them with the fungicides. After 24 hours, they poked holes in the apples with toothpicks and added 20 microliters of liquid containing M. fructicola cells to the poked holes. They placed the apples in a humid chamber for 5 days and measured the number and size of brown rot spots that developed on their surfaces. 

Despite decreasing MfCYP51 expression, ​the researchers found that P. chlororaphis treatment alone didn’t reduce brown rot disease compared to the control, but the DMI fungicide and B. subtilis treatments did. The mixed treatments of DMI fungicide + P. chlororaphis and DMI fungicide + B. subtilis both reduced brown rot disease compared to the control, but only the DMI fungicide + P. chlororaphis treatment prevented it. 

​The researchers concluded that although biofungicides may not be fully effective on their own, commercializing them could allow farmers to use less DMI fungicide. They explained that scaling back the use of DMI fungicides would slow M. fructicola resistance. Finally, they proposed that future researchers test biofungicide mixtures containing pyrrolnitrin in the field to observe real-world effects on stone fruit trees.

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