The spike S of SARS-CoV-2 recognizes ACE2 on the host cell membrane to initiate entry. Soluble decoy receptors, in which the ACE2 ectodomain is engineered to block S with high affinity, potently neutralize infection and, because of close similarity with the natural receptor, hold out the promise of being broadly active against virus variants without opportunity for escape. Here, we directly test this hypothesis. We find that an engineered decoy receptor, sACE22.v2.4, tightly binds S of SARS-associated viruses from humans and bats, despite the ACE2-binding surface being a region of high diversity. Saturation mutagenesis of the receptor-binding domain followed by in vitro selection, with wild-type ACE2 and the engineered decoy competing for binding sites, failed to find S mutants that discriminate in favor of the wild-type receptor. We conclude that resistance to engineered decoys will be rare and that decoys may be active against future outbreaks of SARS-associated betacoronaviruses.

The spike S of SARS-CoV-2 recognizes ACE2 on the host cell membrane to initiate entry. Soluble decoy receptors, in which the ACE2 ectodomain is engineered to block S with high affinity, potently neutralize infection and, due to close similarity with the natural receptor, hold out the promise of being broadly active against virus variants without opportunity for escape. Here, we directly test this hypothesis. We find an engineered decoy receptor, sACE22.v2.4, tightly binds S of SARS-associated viruses from humans and bats, despite the ACE2-binding surface being a region of high diversity. Saturation mutagenesis of the receptor-binding domain followed by in vitro selection, with wild type ACE2 and the engineered decoy competing for binding sites, failed to find S mutants that discriminate in favor of the wild type receptor. We conclude that resistance to engineered decoys will be rare and that decoys may be active against future outbreaks of SARS-associated betacoronaviruses.

Degraded lands are defined by soils that have lost primary productivity due to abiotic or biotic stresses. Among the abiotic stresses, drought, salinity, and heavy metals are the main threats in tropical areas. These stresses affect plant growth and reduce their productivity. Nitrogen-fixing plants such as actinorhizal species that are able to grow in poor and disturbed soils are widely planted for the reclamation of such degraded lands. It has been reported that association of soil microbes especially the nitrogen-fixing bacteria Frankia with these actinorhizal plants can mitigate the adverse effects of abiotic and biotic stresses. Inoculation of actinorhizal plants with Frankia significantly improves plant growth, biomass, shoot and root N content, and survival rate after transplanting in fields. However, the success of establishment of actinorhizal plantation in degraded sites depends upon the choice of effective strains of Frankia. Studies related to the beneficial role of Frankia on the establishment of actinorhizal plants in degraded soils are scarce. In this review, we describe some examples of the use of Frankia inoculation to improve actinorhizal plant performances in harsh conditions for reclamation of degraded lands.

Monoclonal antibodies targeting the SARS‐CoV‐2 spike (S) neutralize infection and are efficacious for the treatment of COVID‐19. However, SARS‐CoV‐2 variants, notably sublineages of B.1.1.529/omicron, have emerged that escape antibodies in clinical use. As an alternative, soluble decoy receptors based on the host entry receptor ACE2 broadly bind and block S from SARS‐CoV‐2 variants and related betacoronaviruses. The high‐affinity and catalytically active decoy sACE2 2 .v2.4‐IgG1 was previously shown to be effective against SARS‐CoV‐2 variants when administered intravenously. Here, inhalation of aerosolized sACE2 2 .v2.4‐IgG1 increased survival and ameliorated lung injury in K18‐hACE2 mice inoculated with P.1/gamma virus. Loss of catalytic activity reduced the decoy's therapeutic efficacy, which was further confirmed by intravenous administration, supporting dual mechanisms of action: direct blocking of S and turnover of ACE2 substrates associated with lung injury and inflammation. Furthermore, sACE2 2 .v2.4‐IgG1 tightly binds and neutralizes BA.1, BA.2, and BA.4/BA.5 omicron and protects K18‐hACE2 mice inoculated with a high dose of BA.1 omicron virus. Overall, the therapeutic potential of sACE2 2 .v2.4‐IgG1 is demonstrated by the inhalation route and broad neutralization potency persists against highly divergent SARS‐CoV‐2 variants.

Monoclonal antibodies targeting the SARS-CoV-2 spike (S) glycoprotein neutralize infection and are efficacious for the treatment of mild-to-moderate COVID-19. However, SARS-CoV-2 variants have emerged that partially or fully escape monoclonal antibodies in clinical use. Notably, the BA.2 sublineage of B.1.1.529/omicron escapes nearly all monoclonal antibodies currently authorized for therapeutic treatment of COVID-19. Decoy receptors, which are based on soluble forms of the host entry receptor ACE2, are an alternative strategy that broadly bind and block S from SARS-CoV-2 variants and related betacoronaviruses. The high-affinity and catalytically active decoy sACE22.v2.4-IgG1 was previously shown to be effective in vivo against SARS-CoV-2 variants when administered intravenously. Here, the inhalation of sACE22.v2.4-IgG1 is found to increase survival and ameliorate lung injury in K18-hACE2 transgenic mice inoculated with a lethal dose of the virulent P.1/gamma virus. Loss of catalytic activity reduced the decoy's therapeutic efficacy supporting dual mechanisms of action: direct blocking of viral S and turnover of ACE2 substrates associated with lung injury and inflammation. Binding of sACE22.v2.4-IgG1 remained tight to S of BA.1 omicron, despite BA.1 omicron having extensive mutations, and binding exceeded that of four monoclonal antibodies approved for clinical use. BA.1 pseudovirus and authentic virus were neutralized at picomolar concentrations. Finally, tight binding was maintained against S from the BA.2 omicron sublineage, which differs from S of BA.1 by 26 mutations. Overall, the therapeutic potential of sACE22.v2.4-IgG1 is further confirmed by inhalation route and broad neutralization potency persists against increasingly divergent SARS-CoV-2 variants.

Deep mutational scanning or deep mutagenesis is a powerful tool for understanding the sequence diversity available to viruses for adaptation in a laboratory setting. It generally involves tracking an in vitro selection of protein sequence variants with deep sequencing to map mutational effects based on changes in sequence abundance. Coupled with any of a number of selection strategies, deep mutagenesis can explore the mutational diversity available to viral glycoproteins, which mediate critical roles in cell entry and are exposed to the humoral arm of the host immune response. Mutational landscapes of viral glycoproteins for host cell attachment and membrane fusion reveal extensive epistasis and potential escape mutations to neutralizing antibodies or other therapeutics, as well as aiding in the design of optimized immunogens for eliciting broadly protective immunity. While less explored, deep mutational scans of host receptors further assist in understanding virus-host protein interactions. Critical residues on the host receptors for engaging with viral spikes are readily identified and may help with structural modeling. Furthermore, mutations may be found for engineering soluble decoy receptors as neutralizing agents that specifically bind viral targets with tight affinity and limited potential for viral escape. By untangling the complexities of how sequence contributes to viral glycoprotein and host receptor interactions, deep mutational scanning is impacting ideas and strategies at multiple levels for combatting circulating and emergent virus strains.

This paper describes a novel approach for purely vision based mobile robot navigation. The visual obstacle avoidance and corridor following behavior rely on the segmentation of the traversable floor region in the omnidirectional robocentric view. The image processing employs a supervised approach in which the segmentation optimal with respect to the appearance of the local environment is determined by cross validation over 3D scans captured by a photonic mixer device (PMD) camera. The range data in the front view provides the seeds and validation data to supervise the appearance based segmentation in the omniview. Segmentation relies on histogram backprojection which maintains separate appearance models for floor, obstacles and background. A naive Bayes classifier predicts the occupancy of the robots local environment by fusing the evidence provided by different segmentations and models. The classification error is analyzed on ground truth data generated by a PMD camera and manually segmented scenes. The scheme is highly robust with respect to ambiguous and misleading visual appearances of obstacles and floor, thus enabling the robot to navigate safely in unstructured environments of diverse appearance, texture and illumination. The proposed vision algorithm and the navigation behavior demonstrate a robust performance in extensive robotic experiments across several hours of autonomous operation.

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