Effect of Conformational Variability on Seasonable Thermal Stability and Cell Entry of Omicron Variants.

The Omicron BA.1 variant of SARS-CoV-2 preferentially infects through the cathepsin-mediated endocytic pathway, but the mechanism of cell entry has not been solved yet because BA.4/5 is more fusogenic and more efficiently spread in human lung cells than BA.2. It has been unclear why the Omicron spike is inefficiently cleaved in virions compared with Delta, and how the relatively effective reproduction proceeds without the cell entry through plasma membrane fusion. Conformational variability from deep neural network-based prediction correlates well with the thermodynamic stability of variants. The difference of seasonable pandemic variants in summer and those in winter is distinguishable by this conformational stability, and the geographical optimization of variants is also traceable. Further, the predicted conformational variability maps rationalize the less efficient S1/S2 cleavage of Omicron variants and provide a valuable insight into the cell entry through the endocytic pathway. It is concluded that conformational variability prediction is able to complement transformation information on motifs in protein structures for drug discovery.

Biomarkers in COVID-19: Association with disease severity and outcomes

Background: In coronavirus disease 2019 (COVID-19) the need for intervention increases with disease severity and a risk prediction model that incorporates biomarkers would be beneficial for identifying patients for treatment escalation. Aim(s): To investigate biomarkers changes associated with disease severity and outcomes (mortality, thrombosis). Method(s): COVID-19 patients were sampled between April 15 and May 31 2020. Disease severity was assessed by World Health Organization (WHO) ordinal scale. 132 systemic biomarkers were investigated by routine and multiplex assays and statistical analysis performed to characterise the biomarker profile of COVID-19 patients associated with disease severity, duration, survival and thrombosis. Result(s): The study enrolled 150 COVID-19 positive adults and 16 healthy volunteers. The average age was 64 years, 59% were male, 85% had co-morbidities, 33% had a thrombotic event, and 13% died. A cross comparative analysis of biomarkers identified 13 biomarkers common to severity, mortality and thrombosis with significant correlation;including endothelial dysfunction (VWF, tPA, TFPI), hypercatabolism (low albumin, Hb, FXIII) and inflammatory response (IL-8, Osteopontin). Similarly, 14 biomarkers associated with severity and mortality included pro-inflammatory cytokines and their receptors (sTNFRII, STNFRI, sIL2a, IL6, MIP1a), neutrophils (elevated WBC, Neutrophils, TIMP1) and tissue remodelling (SCGF, EG3A). Nine biomarkers common across severity and thrombosis were angiogenesis (VEGF, LYVE1, Follistatin), acute phase response (SAP, AGP) and clot formation (Fibrinogen and PAPs). Conclusion(s): The biomarker profile associated with poorer outcomes indicates an inflammatory response, endothelial cell disruption, hypercoagulability and hypercatabolism. This study has identified several biomarkers that may be useful indicators of disease severity and progression. Further work is needed to determine how these may be used to direct clinical management. (Figure Presented).

Psychosocial hazards, posttraumatic stress disorder, complex posttraumatic stress disorder, depression, and anxiety in the U.K. rail industry: A cross-sectional study.

This study examined posttraumatic stress disorder (PTSD), complex PTSD, depression, and anxiety among U.K. rail workers. A cross-sectional survey examining exposure to seven psychosocial hazards (bullying/harassment; verbal abuse; physical and sexual assault; and hearing about, seeing the aftermath of, or witnessing a fatality), working conditions, physical health, and the impact of COVID-19 was administered to 3,912 participants. Outcome measures were the ITQ, PHQ-9, and GAD-7. Among trauma-exposed participants, 24.3% met the criteria for PTSD or CPTSD; 38.6% and 29.2% of all participants scored in the moderate-to-severe range on the PHQ-9 and GAD-7, respectively. Data were analyzed using logistic and linear regression. Bullying/harassment was positively associated with GAD-7 scores, f2 = .001, and PTSD and CPTSD, ORs = 1.83-2.02. Hearing about and witnessing a fatality were associated with PTSD and CPTSD, ORs = 1.77-2.10. Poorer ergonomics at work were positively associated with PHQ-9 and GAD-7 scores, f2 = .001. Higher job satisfaction was associated with lower odds of PTSD and CPTSD, ORs = 0.87-0.91, and negatively associated with PHQ-9 and GAD-7 scores, f2 = .008-.01. Work intensity was associated with PTSD and CPTSD, ORs = 1.79-1.83, and positively associated with PHQ-9 and GAD-7 scores, f2 = .02-.03. Reporting more physical health problems was associated with PTSD, OR = 1.07, and positively associated with GAD-7 and PHQ-9 scores, f2 = .008-.01. The results suggest bullying/harassment and work intensity are important variables in employee mental health and could drive future research and industry initiatives.

Conditionally designed luminescent DNA crystals doped by Ln3+(Eu3+/Tb3+) complexes or fluorescent proteins with smart drug sensing property.

In this work, a designed porous DNA crystal with high intrinsic biocompatibility was used as the scaffold material to load fluorescent guest molecules to detect anti-cancer drugs. It is shown here that the synthesized crystals have the characteristics consistent with the designed large solvent channels, and can therefore accommodate guest molecules such as fluorescent proteins that cannot be accommodated by less porous crystals. Eu(TTA)3phen and Tb(acac)3phen lanthanide complexes were individually noncovalently loaded into the porous crystals, resulting in hybrid luminescent DNA crystals. Emodin, an anti-cancer, anti-tumor, anti-inflammatory drug, was found to quench lanthanide complexes in solution or in crystals. Notably, emodin is the active ingredient of Lianhua Qingwen Capsule, an anti-COVID-19 drug candidate. Therefore, the porous DNA crystals reported here have potential applications as a biocompatible and theranostic delivery biomaterial for functional macromolecules.

SSSCPreds: Deep Neural Network-Based Software for the Prediction of Conformational Variability and Application to SARS-CoV-2.

Amino acid mutations that improve protein stability and rigidity can accompany increases in binding affinity. Therefore, conserved amino acids located on a protein surface may be successfully targeted by antibodies. The quantitative deep mutational scanning approach is an excellent technique to understand viral evolution, and the obtained data can be utilized to develop a vaccine. However, the application of the approach to all of the proteins in general is difficult in terms of cost. To address this need, we report the construction of a deep neural network-based program for sequence-based prediction of supersecondary structure codes (SSSCs), called SSSCPrediction (SSSCPred). Further, to predict conformational flexibility or rigidity in proteins, a comparison program called SSSCPreds that consists of three deep neural network-based prediction systems (SSSCPred, SSSCPred100, and SSSCPred200) has also been developed. Using our algorithms we calculated here shows the degree of flexibility for the receptor-binding motif of SARS-CoV-2 spike protein and the rigidity of the unique motif (SSSC: SSSHSSHHHH) at the S2 subunit and has a value independent of the X-ray and Cryo-EM structures. The fact that the sequence flexibility/rigidity map of SARS-CoV-2 RBD resembles the sequence-to-phenotype maps of ACE2-binding affinity and expression, which were experimentally obtained by deep mutational scanning, suggests that the identical SSSC sequences among the ones predicted by three deep neural network-based systems correlate well with the sequences with both lower ACE2-binding affinity and lower expression. The combined analysis of predicted and observed SSSCs with keyword-tagged datasets would be helpful in understanding the structural correlation to the examined system.

Conformational Variability Correlation Prediction of Transmissibility and Neutralization Escape Ability for Multiple Mutation SARS-CoV-2 Strains using SSSCPreds.

Identifying the fundamental cause of transmissibility of multiple mutation strains and vaccine nullification is difficult in general and is a source of significant concern. The conformational variability of the mutation sites for B.1.617.2 (Δ), B.1.617.1 (κ), B.1.427/429 (ε), P.1 (γ), B.1.351 (ß), B.1.1.7 (α), S477N, and the wild-type strain has been assessed using a deep neural-network-based prediction program of conformational flexibility or rigidity in proteins (SSSCPreds). We find that although the conformation of G614 is rigid, which is assigned as a left-handed (LH) α-helix-type one, that of D614 is flexible without the hydrogen bonding latch to T859. The rigidity of glycine, which stabilizes the local conformation more effectively than that of aspartic acid with the latch, thereby contributes to the reduction of S1 shedding, high expression, and increase in infectivity. The finding that the sequence flexibility/rigidity map pattern of B.1.1.7 is similar to that of the wild-type strain but is largely different from those of B.1.351 and P.1 correlates with the minor escape ability of B.1.1.7. The increased rigidity of the amino acid sequence YRYRLFR from the SSSCPreds data of B.1.427/429 near the L452R mutation site contributes to the 2-fold increased B.1.427/B.1.429 viral shedding in vivo and the increase in transmissibility relative to wild-type circulating strains in a similar manner to D614G. The concordance and rigidity ratios of multiple mutation strains such as B.1.617.2 against the wild-type one at the receptor-binding domain (RBD) and receptor-binding motif (RBM) regions provide a good indication of the transmissibility and neutralization escape ability except for binding affinity of mutation sites such as N501Y.

Projected ICU and Mortuary load due to COVID-19 in Sydney (preprint)

The spread of COVID-19 is expected to put a large strain on many hospital resources, including ICU bed space, and mortuary capacity. In this report we study the possible demands on ICU and mortuary capacity in Sydney, Australia, using an adapted SEIR epidemiological model.