MIT shows how small eddies play a role in feeding ocean microbes; could mitigate some climate change effects

This video still of the North Pacific Ocean shows phosphate nutrient concentrations at 500 meters below the ocean surface. The swirls represent small eddies transporting phosphate from the nutrient-rich equator (lighter colors), northward toward the nutrient-depleted subtropics (darker colors). Credits:Image: Courtesy of the researchersSubtropical gyres are enormous rotating ocean currents that generate sustained circulations in the Earth’s subtropical regions just to the north and south of the equator. These gyres are slow-moving whirlpools that circulate within massive basins around the world, gathering up nutrients, organisms, and sometimes trash, as the currents rotate from coast to coast.

For years, oceanographers have puzzled over conflicting observations within subtropical gyres. At the surface, these massive currents appear to host healthy populations of phytoplankton — microbes that feed the rest of the ocean food chain and are responsible for sucking up a significant portion of the atmosphere’s carbon dioxide.

But judging from what scientists know about the dynamics of gyres, they estimated the currents themselves wouldn’t be able to maintain enough nutrients to sustain the phytoplankton they were seeing. How, then, were the microbes able to thrive?

Now, MIT investigators have found that phytoplankton may receive deliveries of nutrients from outside the gyres and that the delivery vehicle is in the form of eddies — much smaller currents that swirl at the edges of a gyre. These eddies pull nutrients in from high-nutrient equatorial regions and push them into the center of a gyre, where the nutrients are then taken up by other currents and pumped to the surface to feed phytoplankton. 

Ocean eddies, the team found, appear to be an important source of nutrients in subtropical gyres. Their replenishing effect, which the researchers call a “nutrient relay,” helps maintain populations of phytoplankton, which play a central role in the ocean’s ability to sequester carbon from the atmosphere. While climate models tend to project a decline in the ocean’s ability to sequester carbon over the coming decades, this “nutrient relay” could help sustain carbon storage over the subtropical oceans.

“There’s a lot of uncertainty about how the carbon cycle of the ocean will evolve as the climate continues to change, ” says Mukund Gupta, a postdoc at Caltech who led the study as a graduate student at MIT. “As our paper shows, getting the carbon distribution right is not straightforward, and depends on understanding the role of eddies and other fine-scale motions in the ocean.”

The study’s co-writers are Jonathan Lauderdale, Oliver Jahn, Christopher Hill, Stephanie Dutkiewicz, and Michael Follows at MIT, and Richard Williams at the University of Liverpool.

A snowy puzzle

A cross-section of an ocean gyre resembles a stack of nesting bowls that is stratified by density: Warmer, lighter layers lie at the surface, while colder, denser waters make up deeper layers. Phytoplankton lives within the ocean’s top sunlit layers, where the microbes require sunlight, warm temperatures, and nutrients to grow.

When phytoplankton dies, they sink through the ocean’s layers as “marine snow.” Some of this snow releases nutrients back into the current, where they are pumped back up to feed new microbes. The rest of the snow sinks out of the gyre, down to the deepest layers of the ocean. The deeper the snow sinks, the more difficult it is for it to be pumped back to the surface. The snow is then trapped, or sequestered, along with any unreleased carbon and nutrients.

Oceanographers thought that the main source of nutrients in subtropical gyres came from recirculating marine snow. But as a portion of this snow inevitably sinks to the bottom, there must be another source of nutrients to explain the healthy populations of phytoplankton at the surface. Exactly what that source is “has left the oceanography community a little puzzled for some time,” Gupta says.

Swirls at the edge

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In their new study, the team sought to simulate a subtropical gyre to see what other dynamics may be at work. They focused on the North Pacific gyre, one of the Earth’s five major gyres, which circulates over most of the North Pacific Ocean and spans more than 20 million square kilometers. 

The team started with the MITgcm, a general circulation model that simulates the physical circulation patterns in the atmosphere and oceans. To reproduce the North Pacific gyre’s dynamics as realistically as possible, the team used a MITgcm algorithm, previously developed at NASA and MIT, which tunes the model to match actual observations of the ocean, such as ocean currents recorded by satellites, and temperature and salinity measurements taken by ships and drifters.  

“We use a simulation of the physical ocean that is as realistic as we can get, given the machinery of the model and the available observations,” Lauderdale says.

The realistic model captured finer details, at a resolution of fewer than 20 kilometers per pixel, compared to other models that have a more limited resolution. The team combined the simulation of the ocean’s physical behavior with the Darwin model — a simulation of microbe communities such as phytoplankton, and how they grow and evolve with ocean conditions.

The team ran the combined simulation of the North Pacific gyre for over a decade, and created animations to visualize the pattern of currents and the nutrients they carried, in and around the gyre. What emerged were small eddies that ran along the edges of the enormous gyre and appeared to be rich in nutrients.

“We were picking up on little eddy motions, basically like weather systems in the ocean,” Lauderdale says. “These eddies were carrying packets of high-nutrient waters, from the equator, north into the center of the gyre and downwards along the sides of the bowls. We wondered if these eddy transfers made an important delivery mechanism.”

Surprisingly, the nutrients first move deeper, away from the sunlight, before being returned upwards where the phytoplankton live. The team found that ocean eddies could supply up to 50 percent of the nutrients in subtropical gyres.

“That is very significant,” Gupta says. “The vertical process that recycles nutrients from marine snow is only half the story. The other half is the replenishing effect of these eddies. As subtropical gyres contribute a significant part of the world’s oceans, we think this nutrient relay is of global importance.”

UC Riverside's coarse-grained models of coronavirus formation could inform the design of effective drugs to fight SARS-CoV-2

Roya Zandi (left) and Siyu Li. (UCR/Zandi lab)A physicist at the University of California, Riverside, and her graduate student have successfully modeled the formation of SARS-CoV-2, the virus that spreads COVID-19, for the first time.

In a paper published in Viruses, a journal, Roya Zandi, a professor of physics and astronomy at UCR, and Siyu Li, a postdoctoral researcher at Songshan Lake Materials Laboratory in China, offer an overall understanding of the assembly and formation of SARS-CoV-2 from its constituent components.

“Understanding viral assembly has always been a key step leading to therapeutic strategies,” Zandi said. “Numerous experiments and simulations of viruses such as HIV and hepatitis B virus have had a remarkable impact on elucidating their assembly and providing means to combat them. Even the simplest questions regarding the formation of SARS-CoV-2 remain unanswered.”

Zandi explained that a critical step in the life cycle of any virus is the packaging of its genome into new virions or virus particles. This is an especially challenging task for coronaviruses, like SARS-CoV-2, with their very large RNA genomes. Indeed, coronaviruses have the largest genome known for a virus that uses RNA as its genetic material. 

SARS-CoV-2 has four structural proteins: Envelope (E), Membrane (M), Nucleocapsid (N), and Spike (S). The structural proteins M, E, and N are essential for the assembly and formation of the viral envelope — the outermost layer of the virus that protects the virus and helps facilitate entry into host cells. This process occurs at the membrane of the Endoplasmic Reticulum Golgi Intermediate Compartment, or ERGIC, a complex membrane system that provides the coronavirus its lipid envelope. The assembly of coronaviruses is unique compared to many other viruses as this process occurs at the ERGIC membrane. 

Most computational studies to date use coarse-grained models were only details relevant at large length scales are used to mimic viral components. Over the years, the coarse-grained models have explained several virus assembly processes leading to important discoveries.

“In this paper, using coarse-grained models, we have been able to successfully model the formation of SARS-CoV-2: the N proteins condense the RNA to form the compact ribonucleoprotein complex, an assembly of molecules containing both protein and RNA,” Zandi said. “This complex interacts with the M proteins that are embedded in the lipid membrane.”

She added that “budding,” which is when a part of the membrane starts to curve up, completes the virus formation. The model Zandi and Li developed allowed them to explore mechanisms of protein oligomerization, RNA condensation by structural proteins, and cellular membrane-protein interactions. It also allowed them to predict the factors that control virus assembly. 

“Our work reveals key ingredients and components contributing to the packaging of the long genome of SARS-CoV-2,” Li said. “The experimental studies regarding the specific role of each of the several structural proteins involved in the formation of viral particles are soaring but many details remain unclear.” 

According to Zandi, the insight presented in the research paper and the comparison of the findings with those observed experimentally could provide some of these details and inform the design of effective antiviral drugs to arrest coronaviruses in the assembly stage. 

“The physical aspects of coronavirus assembly explored within our model are of interest not just to physical scientists beginning to apply physics-based methods to the study of enveloped viruses, but also to virologists attempting to locate the key protein interactions in virus assembly and budding,” she said. “We now have a better understanding of what interactions are important for the packaging of the genome and the formation of the virus. This is the first time we have been able to fine-tune the interaction between the genome and proteins and obtain the genome condensation and the assembly simultaneously.”

The research was funded by the National Science Foundation and the University of California Multicampus Research Programs and Initiatives. 

The title of the paper is “Biophysical Modeling of SARS-CoV-2 Assembly: Genome Condensation and Budding.”

UK climate expert links the changes in the length of day with climate prediction

web Earth rh 218xfree 4a640UK scientists have made a fundamental breakthrough in the quest to predict fluctuations in the rotation of the Earth accurately and so the length of the day - potentially opening up new predictions for the effects of climate change. 

A team of scientists, led by Professor Adam Scaife from the University of Exeter, has used state-of-the-art mathematical modeling to show how fluctuations in the length of the day can be predicted more than a year in advance – significantly longer than currently possible. 

The team suggests this long-range forecasting also originates from a new atmospheric source for long-range predictability of weather and climate changes.                                                                                                              

Crucially, the research shows a definitive link between geodesy – or accurately measuring and understanding the shape, size, orientation, and gravity on Earth – and climate prediction. 

Professor Scaife, a climate expert from the University of Exeter’s Mathematics department said: “While the changes in day length are tiny, they are important for applications that require very accurate time measurements like GPS.” 

Angular momentum has long been known to play a fundamental role in the structure and variability of the Earth’s atmosphere. 

As the Earth spins around its axis, its overall mass and rotation result in what appears to be a steady rotation. However, surface wind changes and changes in high and low-pressure patterns can change this and if the atmosphere speeds up due to stronger winds, the Earth’s rotation consequently slows down, causing the length of day to increase.   

However, until now the long-range predictability of these fluctuations in the length of the day was unknown. 

The new study shows that fluctuations in atmospheric angular momentum and the length of day are predictable out to more than a year ahead and that the atmospheric changes have an important influence on regional weather and climate.  

Using a range of forecasts from a dynamical climate model, the scientists could predict signals in the atmosphere that spread slowly and coherently towards the poles.  

These signals precede changes in extratropical climate via the North Atlantic Oscillation and the extratropical jet stream. These new findings point to a source of long-range predictability from within the atmosphere that will help us to understand and better predict weather and climate. 

Professor Scaife added: “We usually look to the ocean for long-range prediction signals but these new results show that long-range forecasts can also be driven from within the atmosphere.” 

UK researcher uses ML to predict the biological properties of the most abundant enzyme on Earth 

Ripe Golden Wheat grid f6afaRubisco (Ribulose-1,5-bisphosphate carboxylase/oxygenase) is responsible for providing carbon for almost all life on Earth. Rubisco functions by converting atmospheric CO2 from the Earth’s atmosphere to organic carbon matter, which is essential to sustain most life on Earth. 

For some time now, natural variation has been observed among Rubisco proteins of land plants and modeling studies have shown that transplanting Rubisco proteins with certain functional properties can increase the amount of atmospheric CO2 crop plants can uptake and store.

Study lead author, Wasim Iqbal, a Ph.D. researcher at Newcastle University’s School of Natural and Environmental Sciences, part of Dr. Maxim Kapralov’s group, developed a machine learning tool that can predict the performance properties of numerous land plant Rubisco proteins with surprisingly good accuracy. The hope is that this tool will enable the hunt for a ‘supercharged’ Rubisco protein that can be bioengineered into major crops such as wheat.

Published in the Journal Of Experimental Botany, the study presents a useful tool for screening and predicting plant Rubisco kinetics for engineering efforts as well as for fundamental studies on Rubisco evolution and adaptation. Screening the natural diversity of Rubisco kinetics is the main strategy used to find better Rubiscos for crop engineering efforts.

Improving the accuracy of global photosynthesis estimates

Wasim said: “Our study will have huge implications for climate models and bioengineering crops.

“This study provides plant biologists with a pre-screening tool for highlighting Rubisco species exhibiting better kinetics for crop engineering efforts.

“The machine learning tool can be used to improve the accuracy of global photosynthesis estimates. The Rubisco performance properties our model predicts are compatible with Earth system models (ESM) used by climate scientists. Currently, ESMs use a single set of Rubisco properties from the same species (or sometimes a handful) for estimating photosynthesis at the ecosystem scale. Our machine learning tool could provide predictions for most land plants improving the accuracy of ESMs.”

The next steps of this work include isolating the best Rubisco proteins identified from predictions in the lab and attempting to bioengineer a plant species with a foreign Rubisco protein.

UK scientists use ML to help fight antibiotic resistance in farmed chickens

Dr Tania Dottorini from the School of Veterinary Medicine and Science and Future Food BeaconUniversity of Nottingham scientists have used machine learning to find new ways to identify and pinpoint diseases in poultry farms, which will help to reduce the need for antibiotic treatment, lowering the risk of antibiotic resistance transferring to human populations.

The study was led by Dr. Tania Dottorini from the School of Veterinary Medicine and Science and Future Food Beacon at the University of Nottingham. The research is part of the FARMWATCH project, a £1.5m partnership between the University and the China National Center for Food Safety Risk Assessment.

The rapid increase in poultry production to meet growing demand in China has resulted in the extensive and indiscriminate use of antibiotics. This has led to a worrying increase in cases of antimicrobial resistance (AMR) diagnosed in animals which could potentially spread to humans, via direct contact, environmental contamination, and food consumption.

With antibiotic resistance now one of the most threatening issues worldwide, effective and rapid diagnostics of bacterial infection in chicken farming can reduce the need for antibiotics, which will reduce epidemics and AMR.

In this project, researchers in Nottingham collected samples from the animals, humans, and environment in a Chinese farm and connected slaughterhouse. This complex ‘big’ data has now been analyzed for new diagnostic biomarkers that will predict and detect a bacterial infection, the insurgence of AMR, and transfer to humans. This data will then allow early intervention and treatment, reducing the spread and the need for antibiotics.

The study produced three key findings. Firstly, several similar clinically relevant antimicrobial resistance genes (ARGs) and associated mobile genetic elements (antibiotic resistance genes able to move within genomes and between bacteria), were found in both human and broiler chicken samples. In particular, eleven types of clinically important antibiotic resistance genes, with conserved mobile ARG gene structures were found between samples from different hosts.

Dr Dottorini said: “These similarities would have been missed if we only used large-scale conventional comparative analysis, which, in fact, showed that microbiome and resistomes differ across environments and hosts. Overall, this finding suggests the relevance of adopting a multi-scale analysis when dissecting similarities and differences of resistomes and microbiomes in complex interconnected environments.”

Secondly, the study showed that by developing a machine learning-powered approach integrating metagenomics data with culture-based methods, the team found the existence of a core chicken gut resistome that is correlated with the AMR circulating in the farms. These results supported the hypothesis that correlations exist between resistance phenotypes of individual commensal and pathogenic bacteria and the types of ARGs in the resistome in which they exist in.

Finally, using sensing technology and machine learning, the team uncovered that the AMR-related core resistome is associated with various external factors such as temperature and humidity.

Dr. Dottorini said: “The food production industry represents a major consumer of antibiotics, but the AMR risks within these environments are still not fully understood. It is therefore critical to set out studies and improved methods optimized to these environments where animals and humans may be in close contact. Precision farming, cost-effective DNA sequencing, and the increased adoption of machine learning technologies offer the opportunity to develop methods giving a better understanding and quantification of AMR risks in farming environments.”