Rational strategies for enhancing mAb binding to SARS-CoV-2 variants through CDR diversification and antibody-escape prediction.

Since the emergence of SARS-CoV-2, dozens of variants of interest and half a dozen variants of concern (VOCs) have been documented by the World Health Organization. The emergence of these VOCs due to the continuous evolution of the virus is a major concern for COVID-19 therapeutic antibodies and vaccines because they are designed to target prototype/previous strains and lose effectiveness against new VOCs. Therefore, there is a need for time- and cost-effective strategies to estimate the immune escape and redirect therapeutic antibodies against newly emerging variants. Here, we computationally predicted the neutralization escape of the SARS-CoV-2 Delta and Omicron variants against the mutational space of RBD-mAbs interfaces. Leveraging knowledge of the existing RBD-mAb interfaces and mutational space, we fine-tuned and redirected CT-p59 (Regdanvimab) and Etesevimab against the escaped variants through complementarity-determining regions (CDRs) diversification. We identified antibodies against the Omicron lineage BA.1 and BA.2 and Delta variants with comparable or better binding affinities to that of prototype Spike. This suggests that CDRs diversification by hotspot grafting, given an existing insight into the Ag-Abs interface, is an exquisite strategy to redirect antibodies against preselected epitopes and combat the neutralization escape of emerging SARS-CoV-2 variants.

A potential antibody repertoire diversification mechanism through tyrosine sulfation for biotherapeutics engineering and production.

The diversity of three hypervariable loops in antibody heavy chain and light chain, termed the complementarity-determining regions (CDRs), defines antibody's binding affinity and specificity owing to the direct contact between the CDRs and antigens. These CDR regions typically contain tyrosine (Tyr) residues that are known to engage in both nonpolar and pi stacking interaction with antigens through their complementary aromatic ring side chains. Nearly two decades ago, sulfotyrosine residue (sTyr), a negatively charged Tyr formed by Golgi-localized membrane-bound tyrosylprotein sulfotransferases during protein trafficking, were also found in the CDR regions and shown to play an important role in modulating antibody-antigen interaction. This breakthrough finding demonstrated that antibody repertoire could be further diversified through post-translational modifications, in addition to the conventional genetic recombination. This review article summarizes the current advances in the understanding of the Tyr-sulfation modification mechanism and its application in potentiating protein-protein interaction for antibody engineering and production. Challenges and opportunities are also discussed.

A robust tracking system for COVID-19 like pandemic using advanced hybrid technologies

In order to track patients in coronavirus (COVID-19) like pandemic, this paper proposes a novel model based on hybrid advance technologies, which is capable to trace and track COVID-19 affectees with high accuracy. The hybrid technologies include, cellular, cyber and low range wireless technologies. This technique is capable to trace patients through call data record using cellular technology, voice over Internet protocol calls using cyber technology and physical contact without having a call history using low range wireless technologies. The proposed model is also capable to trace COVID-19 suspects. In addition to tracking, the proposed model is capable to provide surveillance capability as well by geo tagging the patients. In case of any violation by the patients an alert is sent to the concerned department. The proposed model is cost effective and privacy preserved as the entire process is carried out under the umbrella of a concerned government department. The potential outcomes of the proposed model are tracking of COVID-19 patients, monitoring of isolated patients, tracking of suspected ones and inform the mass about the safest path to use. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Manufacture and Characterization of Good Manufacturing Practice-Compliant SARS-COV-2 Cytotoxic T Lymphocytes.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 virus-specific cytotoxic T-cell lymphocytes (vCTLs) could provide a promising modality in COVID-19 treatment. We aimed to screen, manufacture, and characterize SARS-CoV-2-vCTLs generated from convalescent COVID-19 donors using the CliniMACS Cytokine Capture System (CCS). METHODS: Donor screening was done by stimulation of convalescent COVID-19 donor peripheral blood mononuclear cells with viral peptides and identification of interferonγ (IFN-γ)+ CD4 and CD8 T cells using flow cytometry. Clinical-grade SARS-CoV-2-vCTLs were manufactured using the CliniMACS CCS. The enriched SARS-CoV-2-vCTLs were characterized by T-cell receptor sequencing, mass cytometry, and transcriptome analysis. RESULTS: Of the convalescent donor blood samples, 93% passed the screening criteria for clinical manufacture. Three validation runs resulted in enriched T cells that were 79% (standard error of the mean 21%) IFN-γ+ T cells. SARS-CoV-2-vCTLs displayed a highly diverse T-cell receptor repertoire with enhancement of both memory CD8 and CD4 T cells, especially in CD8 TEM, CD4 TCM, and CD4 TEMRA cell subsets. SARS-CoV-2-vCTLs were polyfunctional with increased gene expression in T-cell function, interleukin, pathogen defense, and tumor necrosis factor superfamily pathways. CONCLUSIONS: Highly functional SARS-CoV-2-vCTLs can be rapidly generated by direct cytokine enrichment (12 hours) from convalescent donors. CLINICAL TRIALS REGISTRATION: NCT04896606.

CoV2-TCR: A web server for screening TCR CDR3 from TCR immune repertoire of COVID-19 patients and their recognized SARS-CoV-2 epitopes.

Although multiple vaccines have been developed and widely administered, several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been reported to evade immune responses and spread diffusely. Here, 108 RNA-seq files from coronavirus disease 2019 (COVID-19) patients and healthy donors (HD) were downloaded to extract their TCR immune repertoire by MiXCR. Those extracted TCR repertoire were compared and it was found that disease progression was related negatively with diversity and positively with clonality. Specifically, greater proportions of high-abundance clonotypes were observed in active and severe COVID-19 samples, probably resulting from strong stimulation of SARS-CoV-2 epitopes and a continued immune response in host. To investigate the specific recognition between TCR CDR3 and SARS-CoV-2 epitopes, we constructed an accurate classifier CoV2-TCR with an AUC of 0.967 in an independent dataset, which outperformed several similar tools. Based on this model, we observed a huge range in the number of those TCR CDR3 recognizing those different peptides, including 28 MHC-I epitopes from SARS-CoV-2 and 22 immunogenic peptides from SARS-CoV-2 variants. Interestingly, their proportions of high-abundance, low-abundance and rare clonotypes were close for each peptide. To expand the potential application of this model, we established the webserver, CoV2-TCR, in which users can obtain those recognizing CDR3 sequences from the TCR repertoire of COVID-19 patients based on the 9-mer peptides containing mutation site(s) on the four main proteins of SARS-CoV-2 variants. Overall, this study provides preliminary screening for candidate antigen epitopes and the TCR CDR3 that recognizes them, and should be helpful for vaccine design on SARS-CoV-2 variants.

Variability of mobile phone network logins in the Bialowieza National Park during the 2019 and 2020 summer holiday periods in the context of the COVID-19 pandemic

This article analyses the variability of the number of mobile phone network users in and around the Bialowieza National Park during the summer holidays in 2019 and 2020 based on Call Detail Records. The data collected was used to analyse the mobility of Poles in the study area during the SARS CoV 2 pandemic. The research shows that despite the area's peripheral location and the prevailing pandemic, the number of users of the mobile network in the summer of 2020 increased compared with 2019 - by 20.2% in July and 14.3% in August. The increase in users logged in results from the increased number of tourists who were looking for remote places that could afford them some protection against SARS-CoV-2 infection in the summer of 2020.

Optimizing Antibody Affinity and Developability Using a Framework-CDR Shuffling Approach-Application to an Anti-SARS-CoV-2 Antibody.

The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity. To illustrate this approach, we selected a representative SARS-CoV-2-neutralizing antibody, H4, which was identified and isolated previously based on the predominant germlines that were employed in a human host to target the SARS-CoV-2-human ACE2 receptor interaction. Compared to screening vast CDR libraries for affinity enhancements, our approach identified fewer than 100 antibody framework-CDR combinations, from which we screened and selected an antibody (CB79) that showed a reduced dissociation rate and improved affinity against the SARS-CoV-2 spike protein (7-fold) when compared to H4. The improved affinity also translated into improved neutralization (>75-fold improvement) of SARS-CoV-2. Our rapid and robust approach for optimizing antibodies from parts without the need for tedious structure-guided CDR optimization will have broad utility for biotechnological applications.

Non-structure protein ORF1ab (NSP8) in SARS-CoV-2 contains potential γδT cell epitopes.

Upon activation by the pathogen through T-cell receptors (TCRs), γδT cells suppress the pathogenic replication and thus play important roles against viral infections. Targeting SARS-CoV-2 via γδT cells provides alternative therapeutic strategies. However, little is known about the recognition of SARS-CoV-2 antigens by γδT cells. We discovered a specific Vγ9/δ2 CDR3 by analyzing γδT cells derived from the patients infected by SARS-CoV-2. Using a cell model exogenously expressing γδ-TCR established, we further screened the structural motifs within the CDR3 responsible for binding to γδ-TCR. Importantly, these sequences were mapped to NSP8, a non-structural protein in SARS-CoV-2. Our results suggest that NSP8 mediates the recognition by γδT cells and thus could serve as a potential target for vaccines.

Single domain antibodies derived from ancient animals as broadly neutralizing agents for SARS-CoV-2 and other coronaviruses.

With severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as an emergent human virus since December 2019, the world population is susceptible to coronavirus disease 2019 (COVID-19). SARS-CoV-2 has higher transmissibility than the previous coronaviruses, associated by the ribonucleic acid (RNA) virus nature with high mutation rate, caused SARS-CoV-2 variants to arise while circulating worldwide. Neutralizing antibodies are identified as immediate and direct-acting therapeutic against COVID-19. Single-domain antibodies (sdAbs), as small biomolecules with non-complex structure and intrinsic stability, can acquire antigen-binding capabilities comparable to conventional antibodies, which serve as an attractive neutralizing solution. SARS-CoV-2 spike protein attaches to human angiotensin-converting enzyme 2 (ACE2) receptor on lung epithelial cells to initiate viral infection, serves as potential therapeutic target. sdAbs have shown broad neutralization towards SARS-CoV-2 with various mutations, effectively stop and prevent infection while efficiently block mutational escape. In addition, sdAbs can be developed into multivalent antibodies or inhaled biotherapeutics against COVID-19.

Toward a fractalomic idiotype/anti-idiotypic paradigm.

The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes whose composite tend to resemble the whole - are found in most natural biological structures including immunoglobulin and antigenic epitopes. Increased knowledge of the fractalomic properties of the idiotype/anti-idiotypic paradigm should help develop a novel and improved simplified artificial model of the immune system. Case in point, the regulation and dampening of antibodies as well as the synergetic recognition of an antigen by multiple idiotypes are both immune mechanisms that require further analysis. An enhanced understanding of these complexities could lead to better data analysis for novel vaccines to improve their sensitivity and specificity as well as open other new doors in the field of immunology.

Controlling the Pandemic of COVID-19 based on Telecommunication Data

Mobile phones as a pervasive tool may have several applications in the field of preventing the spread of infectious diseases. Many countries used it in recent years to control Ebola and SARS in Africa, and today controlling COVID-19 in China and Singapore. Since now an effective way to reduce or stop the spread of the disease is social isolation and quarantine while the mobile phone could be an effective tool for patient control. In this paper, a non-invasive method presented for controlling prevalence of COVID-19 disease. It is based on the records of subscriber's transactions in the mobile network. Many valuable data could be obtained by analyzing these records, such as identifying quarantine offenders during illness, the number of interprovince trips while restrictions were imposed, and the density of patients in different parts of the city or country. © 2022 IEEE.

Hybridoma technology: is it still useful?

The isolation of single monoclonal antibodies (mAbs) against a given antigen was only possible with the introduction of the hybridoma technology, which is based on the fusion of specific B lymphocytes with myeloma cells. Since then, several mAbs were described for therapeutic, diagnostic, and research purposes. Despite being an old technique with low complexity, hybridoma-based strategies have limitations that include the low efficiency on B lymphocyte-myeloma cell fusion step, and the need to use experimental animals. In face of that, several methods have been developed to improve mAb generation, ranging from changes in hybridoma technique to the advent of completely new technologies, such as the antibody phage display and the single B cell antibody ones. In this review, we discuss the hybridoma technology along with emerging mAb isolation approaches, taking into account their advantages and limitations. Finally, we explore the usefulness of the hybridoma technology nowadays.

VAEResTL: A Novel Generative Model for Designing Complementarity Determining Region of Antibody for SARS-CoV-2

The global impact of the COVID-19 pandemic underlines the importance of developing a competent machine learning (ML) approach that can rapidly design therapeutics and prophylactics such as antibodies/nanobodies against novel viral infections despite data shortage problems and sequence complexity. Here, we propose a novel end-to-end deep generative model based on convolutional Variational Autoencoder (VAE), Residual Neural Network (Resnet), and Transfer Learning (TL), named VAEResTL that can competently generate CDR-H3 sequences for a novel target lacking sufficient training data. We further demonstrate that our proposed method generates the third complementarity-determining region (CDR) of the heavy chain (CDR-H3) sequences for designing and developing therapeutic antibodies/nanobodies that can bind to different variants of SARS-CoV-2 despite the shortage of SARS-CoV-2 training data. The predicted CDR-H3 sequences are then screened and filtered for their developability parameters namely viscosity, clearance, solubility, stability, and immunogenicity through several in-silico steps resulting in a list of highly optimized lead candidates.

Development of Big Data-Analysis Pipeline for Mobile Phone Data with Mobipack and Spatial Enhancement

Frequent and granular population data are essential for decision making. Further-more, for progress monitoring towards achieving the sustainable development goals (SDGs), data availability at global scales as well as at different disaggregated levels is required. The high population coverage of mobile cellular signals has been accelerating the generation of large-scale spatiotemporal data such as call detail record (CDR) data. This has enabled resource-scarce countries to collect digital footprints at scales and resolutions that would otherwise be impossible to achieve solely through traditional surveys. However, using such data requires multiple processes, algorithms, and considerable effort. This paper proposes a big data-analysis pipeline built exclusively on an open-source framework with our spatial enhancement library and a proposed open-source mobility analysis package called Mobipack. Mobipack consists of useful modules for mobility analysis, including data anonymization, origin–destination extraction, trip extraction, zone analysis, route interpolation, and a set of mobility indicators. Several implemented use cases are presented to demonstrate the advantages and usefulness of the proposed system. In addition, we explain how a large-scale data platform that requires efficient resource allocation can be con-structed for managing data as well as how it can be used and maintained in a sustainable manner. The platform can further help to enhance the capacity of CDR data analysis, which usually requires a specific skill set and is time-consuming to implement from scratch. The proposed system is suited for baseline processing and the effective handling of CDR data;thus, it allows for improved support and on-time preparation.

Turning mobile big data insights into public health responses in times of pandemics: Lessons learnt from the Democratic Republic of the Congo

In low-income countries like the Democratic Republic of the Congo (DRC)—where data is scarce and national statistics offices often under-resourced—aggregated and anonymised mobile operators’ data can provide vital insights for decision-makers to promptly respond to both prevailing and new pandemics, such as COVID-19. Yet, while research on possible applications of mobile big data (MBD) analytics for COVID-19 is growing, there is still little evidence on how such use cases are actually being adopted by governmental authorities and how MBD insights can effectively be turned into informed public health actions in times of crises. This four-part commentary paper aims to bridge such literature gaps, by sharing lessons learnt from the DRC, whereby Congolese public health authorities, through a steep learning curve, have initiated a public–private sector dialogue with local mobile network operators (MNOs) and their ecosystem partners to leverage population mobility insights for COVID-19 policy-making. After having set the scene on the policy relevance of MBD analytics in the context of the DRC in the first section, the paper will then detail four key enablers that contributed, since March 2020, to accelerate Congolese authorities’ uptake of MBD, thus effectively increasing preparedness for future pandemics. Thirdly, we showcase concreate use-cases where “readiness-to-use” has actually translated into actual “usage” and “adoption” for decision-making, while introducing other use cases currently under development. Finally, we explore challenges when harnessing telco big data for decision-making with the ultimate aim to share lessons to replicate the successes and steer the development of MBD for social good in other low-income countries.

Evidence of SARS-CoV-2-Specific T-Cell-Mediated Myocarditis in a MIS-A Case.

A 26-year-old otherwise healthy man died of fulminant myocarditis. Nasopharyngeal specimens collected premortem tested negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Histopathological evaluation of the heart showed myocardial necrosis surrounded by cytotoxic T-cells and tissue-repair macrophages. Myocardial T-cell receptor (TCR) sequencing revealed hyper-dominant clones with highly similar sequences to TCRs that are specific for SARS-CoV-2 epitopes. SARS-CoV-2 RNA was detected in the gut, supporting a diagnosis of multisystem inflammatory syndrome in adults (MIS-A). Molecular targets of MIS-associated inflammation are not known. Our data indicate that SARS-CoV-2 antigens selected high-frequency T-cell clones that mediated fatal myocarditis.

Analysis of TCR Repertoire by High-Throughput Sequencing Indicates the Feature of T Cell Immune Response after SARS-CoV-2 Infection.

Coronavirus disease 2019 (COVID-19) is a global infectious disease caused by the SARS-CoV-2 coronavirus. T cells play an essential role in the body's fighting against the virus invasion, and the T cell receptor (TCR) is crucial in T cell-mediated virus recognition and clearance. However, little has been known about the features of T cell response in convalescent COVID-19 patients. In this study, using 5'RACE technology and PacBio sequencing, we analyzed the TCR repertoire of COVID-19 patients after recovery for 2 weeks and 6 months compared with the healthy donors. The TCR clustering and CDR3 annotation were exploited to discover groups of patient-specific TCR clonotypes with potential SARS-CoV-2 antigen specificities. We first identified CD4+ and CD8+ T cell clones with certain clonal expansion after infection, and then observed the preferential recombination usage of V(D) J gene segments in CD4+ and CD8+ T cells of COVID-19 patients with different convalescent stages. More important, the TRBV6-5-TRBD2-TRBJ2-7 combination with high frequency was shared between CD4+ T and CD8+ T cells of different COVID-19 patients. Finally, we found the dominant characteristic motifs of the CDR3 sequence between recovered COVID-19 and healthy control. Our study provides novel insights on TCR in COVID-19 with different convalescent phases, contributing to our understanding of the immune response induced by SARS-CoV-2.

Identification of Mpro inhibitors of SARS-CoV-2 using structure based computational drug repurposing.

The recent outbreak of COVID-19, caused by the novel pathogen SARS-coronavirus 2 (SARS-CoV-2) is a severe health emergency. In this pandemic, drug repurposing seems to be the most promising alternative to identify effective therapeutic agents for immediate treatment of infected patients. The present study aimed to evaluate all the drugs present in drug bank as potential novel SARS-CoV-2 inhibitors, using computational drug repurposing studies. Docking-based virtual screening and binding energy prediction were performed, followed by Absorption Distribution Metabolism Excretion calculation. Hydroxychloroquine and Nelfinavir have been identified as the best potential inhibitor against the SARS-CoV-2, therefore, they were used as reference compounds in computational DR studies. The docking study revealed 13 best compounds based on their highest binding affinity, binding energy, and dock score concerning the other screened compounds. Out of 13, only 4 compounds were further shortlisted based on their binding energy and best ADME properties. The hierarchical virtual screening yielded the best 04 drugs, DB07042 (compound 2), DB13035 (compound 3), DB13604 (compound 5) and DB08253 (compound 6), with commendable binding energies in kcal/mol, i.e. -65.45, -62.01, -52.09 and -51.70 respectively. Further, Molecular dynamics simulation with 04 best-retrieved hits has confirmed stable trajectories in protein in terms of root mean square deviation and root mean square fluctuation. During 30 ns simulation, the interactions were also found similar to the docking-based studies. However, clinical studies are necessary to investigate their therapeutic use against this outbreak.

Characterizing the BCR repertoire during lymphocyte reduction and recovery mediated by cyclophosphamide and granulocyte-macrophage colony-stimulating factor.

Leukopenia is a common manifestation of many diseases, including global outbreak SAS-CoV-2 infection. Granulocyte-macrophage colony-stimulating factor (GM -CSF) has been proved to be effective in promoting lymphocyte regeneration, but adverse immunological effects have also emerged. This study aim to investigate the effect of GM -CSF on BCR heavy chain CDR3 repertoire while promoting lymphocyte regeneration. Cyclophosphamide (CTX) and GM -CSF were used to inhibit and stimulate bone marrow hematopoiesis, respectively. High throughput sequencing was applied to detect the characteristics of BCR CDR3 repertoire in controls, CTX group and GM -CSF group. The white blood cells (WBCs) were quickly reduced (P < 0.05) with lymphocytes decreasing causing by CTX, and the WBCs and lymphocytes returned to the level of controls after GM -CSF treatment. The diversity of BCR heavy chain CDR3 repertoire was also significantly decreased in CTX group. Although there is still a big gap from the controls, the diversity was picked up after GM -CSF treatment. The expression of IGHD01-01, IGHD02-14 and IGHJ04-01 with high-frequency usage regularly and significantly changed in three groups, and many genes with low-frequency usage lost in CTX group and did not reappear in GM -CSF group. Moreover, two shared sequences and accounted for the highest proportion in GM -CSF group have been detected in animal model of chronic lymphocytic leukemia. These results revealed that GM -CSF can partially restore changes in the BCR heavy chain CDR3 repertoire while promoting lymphocyte regeneration, but it may also lead to rearrangement, proliferation and activation of abnormal B cells, which can provide a basis for further study on the adverse immunological effects and mechanism of GM -CSF treatment.

Broadly-Neutralizing Antibodies Against Emerging SARS-CoV-2 Variants.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have become a major concern in the containment of current pandemic. The variants, including B.1.1.7 (Alpha), B.1.351 (Beta), P1 (Gamma) and B.1.617.2 (Delta) have shown reduced sensitivity to monoclonal antibodies, plasma and/or sera obtained from convalescent patients and vaccinated individuals. Development of potent therapeutic monoclonal antibodies (mAbs) with broad neutralizing breadth have become a priority for alleviating the devastating effects of this pandemic. Here, we review some of the most promising broadly neutralizing antibodies obtained from plasma of patients that recovered from early variants of SARS-CoV-2 that may be effective against emerging new variants of the virus. This review summarizes several mAbs, that have been discovered to cross-neutralize across Sarbecoviruses and SARS-CoV-2 escape mutants. Understanding the characteristics that confer this broad and cross-neutralization functions of these mAbs would inform on the development of therapeutic antibodies and guide the discovery of second-generation vaccines.