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 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.”