As of late 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants associated with increased transmissibility and/or immune evasion (antibody escape) had nearly completely supplanted the original founder strain (Wu-Hu-1). Emerging variants frequently have at least one mutation in the receptor-binding domain (RBD), which can affect binding to angiotensin-converting enzyme 2 (ACE2). For example, alpha (B.1.1.7), beta, and gamma variants have the N501Y mutation, which is associated with higher affinity binding to ACE2, implying that this could be a selective pressure for variant emergence.
Study: Predictive profiling of SARS-CoV-2 variants by deep mutational learning. Image Credit: Lightspring/Shutterstock
Previous investigations used yeast surface display and deep mutational scanning (DMS) to examine the impact of single-position mutations on binding to ACE2 and monoclonal or serum antibodies on the complete 201 amino acid RBD of SARS-CoV-2. Several widely circulating variants (e.g., beta, gamma, and delta) as well as newly developing variants (e.g., mu (B.1621) and lambda (C.37) have numerous mutations in the RBD, which are associated with improved ACE2 binding and/or multi-class antibody escape.
The recent emergence of the omicron variant with 15 RBD mutations, which poses a significant danger of immune evasion, highlights the urgent need to understand the impact of combinatorial mutations. However, as the number of mutations and amino acid diversity increase, combinatorial sequence space expands exponentially, rapidly exceeding the capabilities of experimental screening procedures. For example, theoretical sequence space greatly exceeds what can be screened by yeast display libraries while focused only on a subset of twenty RBD residues directly involved in ACE2 binding.
Deep mutational learning (DML) is a technique developed by researchers from multiple institutions that combines experimental yeast display screening of RBD mutagenesis libraries with deep sequencing and machine learning. DML allows for a complete analysis of combinatorial RBD mutations and their impact on ACE2 binding and antibody escape, allowing for SARS-CoV-2 variant predictive profiling.
A preprint version of the study is available on the bioRxiv* server while the article undergoes peer review.
The authors examined their classification performance on defined variants, followed by experimental validation and structural modeling, after establishing that ACE2 binding and antibody escape machine learning models can produce highly accurate predictions on test data. To replicate realistic evolutionary routes, synthetic lineages were created in silico, with variants lacking anticipated ACE2-binding intermediates at each mutational stage being discarded. The lineages were created to contain mutations from the original Wu-Hu-1 RBD sequence at edit distance 3 (ED3), ED5, and ED7 (nucleotide and amino acid). The sequences were also chosen to establish lineages with mutations found in circulating variations.
A consensus model was used to predict ACE2 binding, in which a given RBD sequence is projected to bind ACE2 if both the RF and RNN models provide P > 0.5; otherwise, they are anticipated to be non-binders. The 46 synthetic lineage variants were chosen for their ACE2 binding prediction variety (36 predicted binders, ten predicted non-binders). Additionally, predictions for escape from each of the four therapeutic antibodies were established using a similar consensus model technique for the synthetic variations (RBD sequence escapes an antibody when both RF and RNN outputs are P 0.5).
Each synthetic RBD variation was independently produced on the surface of yeast cells and tested for ACE2 binding and antibody escape after all machine learning predictions were completed. The consensus model accurately predicted ACE2 binding for 91.67 % of the synthetic variations, with a non-binding prediction accuracy of 100 %, yielding a prediction accuracy of 93.48 % overall. The cumulative accuracy of antibody escape predictions across all four therapeutic antibodies was 93.94 % for the 33 correctly predicted ACE2-binding variants.
In addition, consensus models predicted ACE2 binding and escape from all four therapeutic antibodies in three variations that were just ED3 (nucleotide and amino acid) from the Wu-Hu-1 RBD. Mutations were found in one of these variations at locations 493, 498, and 501, which are all mutated in the omicron variant. Following yeast display studies, the machine learning predictions of antibody escape from all four therapeutic antibodies, including the often mutation resistant REGN10987, were confirmed. AlphaFold2 was used to perform structural modeling on eight synthetic RBD variants. According to structural predictions, several non-binding ACE2 variants did not differ significantly from the original Wu-Hu-1 RBD. The ACE2-binding variations, on the other hand, displayed a wide range of potential structural conformations.
According to evidence, other endemic coronavirus receptor-binding domains may be undergoing adaptive evolution to avoid human antibody reactions. As a result, combining DML with phylogenetic models of viral evolution to predict SARS-CoV-2 escape from polyclonal antibodies present in the serum of vaccinated or convalescent individuals may enable the identification of future variants with the highest likelihood of emergence and thus support vaccine development for coronavirus disease 2019 (COVID-19).
bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
Taft, J. et al. (2021) “Predictive profiling of SARS-CoV-2 variants by deep mutational learning”. bioRxiv. doi: 10.1101/2021.12.07.471580. https://www.biorxiv.org/content/10.1101/2021.12.07.471580v1
Unraveling How Strigoractone Hormone Regulates Massive Gene Networks Controlling Plant Growth
As sessile organisms, plants have to continually adapt their growth and architecture to the ever-changing environment. To do so, plants have evolved distinct molecular mechanisms to sense and respond to the environment and integrate the signals from outside with endogenous developmental programs.
New research from Nitzan Shabek’s laboratory at the UC Davis College of Biological Sciences, published in Nature Plants, unravels the underlying mechanism of protein targeting and destruction in a specific plant hormone signaling pathway.
Our lab aims at deciphering sensing mechanisms in plants and understanding how specific enzymes function can be regulated at the molecular levels. We have been studying a new plant hormone signal, strigolactone, that governs numerous processes of growth and development including branching and root architecture.”
Nitzan Shabek, assistant professor of biochemistry and structural biology, Department of Plant Biology
The work stems from a study by Shabek, published in Nature in 2018, unravelling molecular and structural changes in an enzyme, MAX2 (or D3) ubiquitin ligase. MAX2 was found in locked or unlocked forms that can recruit a strigolactone sensor, D14, and target for destruction a DNA transcriptional repressor complex, D53. Ubiquitins are small proteins, found in all eukaryotes, that “tag” other proteins for destruction within a cell.
To find the key to unlock MAX2 and to better understand its molecular dynamics in plants, postdoctoral fellows Lior Tal and Malathy Palayam, with junior specialist Aleczander Young, used an approach that integrated advanced structural biology, biochemistry, and plant genetics.
“We leveraged structure-guided approaches to systemically mutate MAX2 enzyme in Arabidopsis and created a MAX2 stuck in an unlocked form”, said Shabek, “some of these mutations were made by guiding CRISPR/Cas9 genome editing thus providing us a discovery platform to study and analyze the different signaling outputs and illuminate the role of MAX2 dynamics.”
They found that in the unlocked conformation, MAX2 can target the repressor proteins and biochemically decorate them with small ubiquitin proteins, tagging them for destruction. Removing these repressors allows other genes to be expressed – activating a massive gene network that governs shoot branching, root architecture, leaf senescence, and symbiosis with fungi, Shabek said.
Sending these repressors to the proteasome disposal complexes requires the enzyme to relock again. The team also showed that MAX2 not only target the repressors proteins, but once it is locked the strigolactone sensor itself gets destroyed, returning the system to its original state.
Finally, the study uncovered the key to the lock, an organic acid metabolite that can directly trigger the conformational switch.
“Beyond the implication in plants signaling, this is the first work that placed a primary metabolite as a direct new regulator of this type of ubiquitin ligase enzymes and will open new avenues of study in this direction,” Shabek said.
Additional coauthors on the paper are specialist Mily Ron and Professor Anne Britt, Department of Plant Biology. The study was supported by NSF CAREER and EAGER grants to Shabek. X-ray crystallography data was obtained at the Advanced Light Source, Lawrence Berkeley National Laboratory, a U.S. Department of Energy user facility.
Tal, L., et al. (2022) A conformational switch in the SCF-D3/MAX2 ubiquitin ligase facilitates strigolactone signalling. Nature Plants. doi.org/10.1038/s41477-022-01145-7.
Original Article: news-medical.net
UrFU Sociologists Identify Digital Fears Among Young People
Sociologists at the Ural Federal University (UrFU) have identified digital fears among young people. According to experts, these are additional fears that do not replace, but complement and reinforce traditional ones. They emerged against the background of uncertainty, the growth of forces beyond human control. Developed emotional intelligence, creativity, and the ability to collaborate help to overcome them.
In the study, sociologists interviewed 1,050 people aged 18-30. Respondents were asked to assess which digital risks concern them most. The study was launched in 2020 and the results were published in April 2022 in the Changing Societies & Personalities journal.
The first group of fears is influence and control. It touches on the problem of interference with privacy by technical means. This category is the most significant for young people: 55.8% are afraid of total control by means of video-surveillance and monitoring software on their mobile devices. 48.5% of respondents believe they are at risk of wiretapping, tracking content in social networks, and inability to keep correspondence secret.”
Natalia Antonova, Professor, Department of Applied Sociology, UrFU
45.8% of young people fear the manipulative influence of the media and an increase in fake news. At the same time, only 27.8% and 18.1% of respondents are concerned about microchipping and genetic manipulation, respectively. It is likely that these threats seem more controllable, both from the individual (through control of food choices, medical procedures, etc.) and from government programs, the researchers believe.
The second group of concerns is crime and security. Here young people are wary of illegal actions using digital technology.
“One of the main fears of 56% of young people is the security of personal data. This is related both to the growth of personal information in social networks and messengers, and to the growth of hacker attacks and viruses. 42.9% of young citizens are afraid of Internet fraudsters, and 25.8% are afraid of losing important information, including smashing their phones, not saving data, forgetting their passwords, or being without an Internet connection,” explains Sofia Abramova, Associate Professor at the Department of Applied Sociology at UrFU.
The third group of fears is based on changes in the way and pace of life, ways of interaction. Thus, 28.4% of respondents indicate a constant lack of time, the acceleration of communications, and worries about not being able to complete all tasks in time. Respondents are also concerned about the growth of online communications and communications with electronic systems (bots, autoresponders, product systems, etc.).
“As a result, 15.3% of young people raise problems related to increasing social distrust against the background of increasing dependence of human life and health on other people and electronic systems: in public transport, planes, elevators, medical intervention,” explains Sofia Abramova.
Respondents also fear the negative consequences of technological development. For example, 22.2% of young citizens fear the robotization of labor processes and the displacement of humans by robots. 14.6% speak directly about negative emotions in relation to the expansion of artificial intelligence.
The fifth type of fear is social inequality. Young people negatively assess the growth of inequality in access to information resources and technology, the exclusion of citizens from the economy depending on the level of digital competence and education, and age. As a result, they fear that benefits will be distributed more and more unequally, both among the inhabitants of the country and between countries.
“It is noteworthy that young people are simultaneously afraid of total surveillance via phone and afraid of being left without mobile devices. Fears shape the irrational behavior of the digital generation, entailing serious transformations in everyday life,” says Natalia Antonova.
Abramova, S.B., et al. (2022) Digital Fears Experienced by Young People in the Age of Technoscience. Changing Societies & Personalities. doi.org/10.15826/csp.2022.6.1.163.
Original Source: news-medical.net
Study demonstrates increased incidence of SARS-CoV-2 Omicron breakthrough infection in cancer patients
In a recently published article in the journal Cancer Cell, scientists have demonstrated the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in cancer patients residing in Austria and Italy. The study reveals an induction in Omicron breakthrough infections in patients with hematologic and solid cancers.
Study: Enhanced SARS-CoV-2 breakthrough infections in patients with hematologic and solid cancers due to Omicron. Image Credit: Lightspring/Shutterstock
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of the coronavirus disease 2019 (COVID-19) pandemic, has been found to cause severe infections in immunocompromised patients, including cancer patients. Moreover, a relatively lower level of neutralizing antibodies in response to COVID-19 vaccines has also been observed in cancer patients, especially those receiving B cell-targeting therapies.
The emergence of SARS-CoV-2 variants with improved immune fitness, such as delta and Omicron variants, has caused a sharp increase in breakthrough infections even in fully vaccinated individuals. However, the vaccines still show high protective efficacy against severe and fatal infections. COVID-19 vaccines have shown acceptable efficacy against severe disease, even in Omicron-infected cancer patients. However, the isolation and quarantine measures associated with SARS-CoV-2 infection may impair the routine administration of anticancer therapy, which can reduce the survival prognosis in cancer patients.
In the current study, the scientists have assessed the incidence of SARS-CoV-2 infection in cancer patients throughout the pandemic.
The study included 3,959 cancer patients, of whom 77% had solid cancer, and 23% had hematologic cancer. About 69% of the patients did not receive any anticancer treatment at the time of COVID-19 vaccination. Regarding vaccine coverage, about 85% of the patients had received at least one vaccine dose, and 15% remained unvaccinated. The incidence of SARS-CoV-2 infection in these patients was assessed between February 2020 and 2022.
SARS-CoV-2 infection was detected in about 24% of the patients during the study period. During the delta-dominated wave, vaccine breakthrough infection was observed in 43% of the patients. In contrast, a significantly higher percentage of breakthrough infection (70%) was observed among the patients during the Omicron-dominated wave. During both delta and Omicron waves, cancer patients receiving systemic anticancer treatment showed a significantly higher percentage of breakthrough infection than those not receiving treatment (83% vs. 56%).
Regarding disease severity irrespective of vaccination status, a higher frequency of COVID-19-related hospitalization was observed during the delta wave compared to that during the Omicron wave. However, a relatively shorter duration of hospital stay was observed in vaccinated patients compared to that in unvaccinated patients. In addition, only 9% of patients with breakthrough infections were admitted to the intensive care unit (ICU). This highlights the protective efficacy of COVID-19 vaccines against severe disease.
Humoral immune response to vaccination
To determine vaccine-induced antibody response against delta and Omicron variants, the scientists measured blood levels of anti-delta and anti-Omicron spike receptor-binding domain (RBD) antibodies in a total of 78 cancer patients. In the analysis, they also included 25 healthcare workers as controls.
In response to vaccination, healthcare workers showed higher levels of total anti-spike antibodies compared to cancer patients. The lowest level of wildtype RBD-specific antibodies was observed in hematologic cancer patients receiving B cell-targeted treatment, followed by hematologic cancer patients not receiving B cell-targeted treatment and patients with solid tumors. A similar trend was observed for delta- and Omicron-specific spike RBD antibodies.
The serum samples collected from hematologic cancer patients without B cell-targeted treatment and solid tumor patients significantly inhibited the interaction between wildtype/delta RBD and angiotensin-converting enzyme 2 (ACE2; host cell receptor for viral entry). However, a significantly lower level of inhibition was observed for patients receiving B cell-targeted treatment. Importantly, a marked reduction in inhibition of Omicron RBD – ACE2 interaction was observed for all patients with solid tumors and hematologic cancer.
The study demonstrates an increased incidence of vaccine breakthrough infections but a reduced disease severity among patients with solid tumors and hematologic cancer during the Omicron wave compared to the delta wave.
The study also highlights that COVID-19 vaccine-induced antibody response is lower in cancer patients than in healthy individuals. The reduction in antibody response is highest among hematologic patients receiving B cell-targeted treatment. Overall, a significant impairment in vaccine-induced Omicron neutralization has been observed in cancer patients.
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