Assessment of S-Specific IgG and IgM Positive Rates in Healthy Hospital Staff Members Vaccinated with the Inactivated SARS-CoV-2 Vaccine.

Widespread vaccination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine makes the assessment of antibodies' positive rates essential. In this study, a total of 378 hospital staff members vaccinated with the vaccine were selected as research subjects. Serum-specific IgG and IgM against the SARS-CoV-2 spike protein (S) were detected, and S-specific IgG and IgM positive rates were analyzed in different age and sex groups, as was the serological pattern of IgG/IgM. The positive rates of IgG and IgM were 92.06% and 44.44%, respectively. The percentage of both IgG and IgM positive (IgG+IgM+) was 43.92%. A total of 182 vaccinees (46.90%) were IgG positive and IgM negative (IgG+IgM-), and 28 vaccinees (7.41%) were negative for both IgG and IgM (IgG-IgM-); 2 participants were positive for IgM alone (IgG-IgM+). In sex subgroups, the rate of IgM positivity was significantly higher in the male group than in the female group (p = 0.027). In different age subgroups, positive rates for IgG in the young group were significantly higher than those in the other group (p = 0.035). Furthermore, ratios of sample values to cutoff values (S/CO values) for IgG in vaccinees who were S-specific IgG positive were compared, and the S/CO values of IgG were significantly higher in the younger group than in the other group (p < 0.001). When comparing the influence of sex on two specific serological patterns (IgG+IgM- and IgG+IgM+), a significant difference in positivity rates was detected (p = 0.011). Male vaccinees were more likely than females to have an IgG+IgM+ pattern.

Potential 3-chymotrypsin-like cysteine protease cleavage sites in the coronavirus polyproteins pp1a and pp1ab and their possible relevance to COVID-19 vaccine and drug development.

Coronavirus (CoV) 3-chymotrypsin (C)-like cysteine protease (3CLpro ) is a target for anti-CoV drug development and drug repurposing because along with papain-like protease, it cleaves CoV-encoded polyproteins (pp1a and pp1ab) into nonstructural proteins (nsps) for viral replication. However, the cleavage sites of 3CLpro and their relevant nsps remain unclear, which is the subject of this perspective. Here, we address the subject from three standpoints. First, we explore the inconsistency in the cleavage sites and relevant nsps across CoVs, and investigate the function of nsp11. Second, we consider the nsp16 mRNA overlapping of the spike protein mRNA, and analyze the effect of this overlapping on mRNA vaccines. Finally, we study nsp12, whose existence depends on ribosomal frameshifting, and investigate whether 3CLpro requires a large number of inhibitors to achieve full inhibition. This perspective helps us to clarify viral replication and is useful for developing anti-CoV drugs with 3CLpro as a target in the current coronavirus disease 2019 (COVID-19) pandemic.

Is lymphopenia different between SARS and COVID-19 patients?

Lymphopenia is commonly observed in SARS and COVID-19 patients although the lymphocyte count is not always below 0.8 × 109 /L in all the patients. It is suggested that lymphopenia serves as a useful predictor for prognosis in the patients. It is also hypothesized that lymphopenia is related to glucocorticoids and apoptosis. However, the ordering between lymphopenia and apoptosis appears different between SARS and COVID-19 patients, ie, lymphopenia is prior to apoptosis in SARS patients whereas apoptosis is prior to lymphopenia in COVID-19 patients. This paper attempts to figure out this contradiction through three players, lymphopenia, glucocorticoids, and apoptosis. Although the literature does not provide a solid explanation, the level of glucocorticoids could determine the ordering between lymphopenia and apoptosis because the administration of high doses of glucocorticoids could lead to lymphopenia whereas low doses of glucocorticoids could benefit patients. In the meantime, this paper raises several questions, which need to be answered in order to better understand the whole course of COVID-19.

Spatial and temporal roles of SARS-CoV PLpro -A snapshot.

SARS-CoV and SARS-CoV-2 encode four structural and accessory proteins (spike, envelope, membrane and nucleocapsid proteins) and two polyproteins (pp1a and pp1ab). The polyproteins are further cleaved by 3C-like cysteine protease (3CLpro ) and papain-like protease (PLpro ) into 16 nonstructural proteins (nsps). PLpro is released from nsp3 through autocleavage, and then it cleaves the sites between nsp1/2, between nsp2/3 and between nsp3/4 with recognition motif of LXGG, and the sites in the C-terminus of ubiquitin and of protein interferon-stimulated gene 15 (ISG15) with recognition motif of RLRGG. Alone or together with SARS unique domain (SUD), PLpro can stabilize an E3 ubiquitin ligase, the ring-finger, and CHY zinc-finger domain-containing 1 (RCHY1), through domain interaction, and thus, promote RCHY1 to ubiquitinate its target proteins including p53. However, a dilemma appears in terms of PLpro roles. On the one hand, the ubiquitination of p53 is good for SARS-CoV because the ubiquitinated p53 cannot inhibit SARS-CoV replication. On the other hand, the ubiquitination of NF-κB inhibitor (IκBα), TNF receptor-associated factors (TRAFs), and stimulator of interferon gene (STING), and the ISGylation of targeted proteins are bad for SARS-CoV because these ubiquitination and ISGylation initiate the innate immune response and antiviral state. This mini-review analyzes the dilemma and provides a snapshot on how the viral PLpro smartly manages its roles to avoid its simultaneously contradictory actions, which could shed lights on possible strategies to deal with SARS-CoV-2 infections.

Liquid Viscosity and Surface Tension of n-Hexane, n-Octane, n-Decane, and n-Hexadecane up to 573 K by Surface Light Scattering (SLS).

In the present study, the simultaneous and accurate determination of liquid viscosity and surface tension of the n-alkanes n-hexane (n-C6H14), n-octane (n-C8H18), n-decane (n-C10H22), and n-hexadecane (n-C16H34) by surface light scattering (SLS) in thermodynamic equilibrium is demonstrated. Measurements have been performed over a wide temperature range from 283.15 K up to 473.15 K for n-C6H14, 523.15 K for n-C8H18, and 573.15 K for n-C10H22 as well as n-C16H34. The liquid dynamic viscosity and surface tension data with average total measurement uncertainties (k = 2) of (2.0 and 1.7) % agree with the available literature and contribute to a new database at high temperatures. Over the entire temperature range, a Vogel-type equation for the dynamic viscosity and a modified van der Waals equation for the surface tension represent the measured data for the four n-alkanes within experimental uncertainties. By also considering our former SLS data for n-dodecane (n-C12H26) and n-octacosane (n-C28H58), empirical models for the liquid viscosity and surface tension of n-alkanes were developed as a function of temperature and carbon number covering values between 6 and 28. Agreement between these models and reference correlations for further selected n-alkanes which were not included in the development procedure was found.

Can Biofilm Be Reversed Through Quorum Sensing in Pseudomonas aeruginosa?

Pseudomonas aeruginosa is a Gram-negative bacterium causing diseases in plants, animals, and humans, and its drug resistance is a major concern in medical care. Biofilms play an important role in P. aeruginosa drug resistance. Three factors are most important to induce biofilm: quorum sensing (QS), bis-(3'-5')-cyclic diguanosine monophosphate (c-di-GMP), and small RNAs (sRNAs). P. aeruginosa has its own specific QS system (PQS) besides two common QS systems, LasI-LasR and RhlI-RhlR, in bacteria. PQS is interesting not only because there is a negative regulation from RhlR to pqsR but also because the null mutation in PQS leads to a reduced biofilm formation. Furthermore, P. aeruginosa dispersed cells have physiological features that are distinct between the planktonic cells and biofilm cells. In response to a low concentration of c-di-GMP, P. aeruginosa cells can disperse from the biofilms to become planktonic cells. These raise an interesting hypothesis of whether biofilm can be reversed through the QS mechanism in P. aeruginosa. Although a single factor is certainly not sufficient to prevent the biofilm formation, it necessarily explores such possibility. In this hypothesis, the literature is analyzed to determine the negative regulation pathways, and then the transcriptomic data are analyzed to determine whether this hypothesis is workable or not. Unexpectedly, the transcriptomic data reveal a negative regulation between lasI and psqR. Also, the individual cases from transcriptomic data demonstrate the negative regulations of PQS with laslI, laslR, rhlI, and rhlR under different experiments. Based on our analyses, possible strategies to reverse biofilm formation are proposed and their clinic implications are addressed.

Proteases HtrA and HtrB for α-amylase secreted from Bacillus subtilis in secretion stress.

HtrA and HtrB are two important proteases across species. In biotechnological industries, they are related to degradation of secreted heterologous proteins from bacteria, especially in the case of overproduction of α-amylases in Bacillus subtilis. Induction of HtrA and HtrB synthesis follows the overproduction of α-amylases in B. subtilis. This is different from the order usually observed in B. subtilis, i.e., the production of proteases is prior to the secretion of proteins. This discrepancy suggests three possibilities: (i) HtrA and HtrB are constantly synthesized from the end of the exponential phase, and then are synthesized more abundantly due to secretion stress; (ii) There is a hysteresis mechanism that holds HtrA and HtrB back from their large amount of secretion before the overproduction of α-amylases; (iii) Heterologous amylases could be a stress to B. subtilis leading to a general response to stress. In this review, we analyze the literature to explore these three possibilities. The first possibility is attributed to the regulatory pathway of CssR-CssS. The second possibility is because sigma factor σD plays a role in the overproduction of α-amylases and is subpopulation dependent with the switch between "ON" and "OFF" states that is fundamental for a bistable system and a hysteresis mechanism. Thus, sigma factor σD helps to hold HtrA and HtrB back from massive secretion before the overproduction of α-amylases. The third possibility is that several sigma factors promote the secretion of proteases at the end of the exponential phase of growth under the condition that heterologous amylases are considered as a stress.

Could ALDH2*2 be the reason for low incidence and mortality of ovarian cancer for East Asia women?

It is curious that East Asian women have a low incidence and mortality of ovarian cancer in various epidemiological studies. Although different explanations were given, they appear unsubstantial. We notice that East Asian population usually are inactive aldehyde dehydrogenase 2 mutation (ALDH2 * 2) carriers, and ALDH plays an important role in the resistance of ovarian cancer to chemotherapeutics, especially in ovarian cancer stem cells. Therefore, we hypothesize whether ALDH2 mutation is the major reason for low incidence and mortality of ovarian cancer in East Asian women, and use the evidence from literature, transcriptomic data with average 5-year overall survival to confirm our hypothesis.

Reorganization of gene network for degradation of polycyclic aromatic hydrocarbons (PAHs) in Pseudomonas aeruginosa PAO1 under several conditions.

Although polycyclic aromatic hydrocarbons (PAHs) are harmful to human health, their elimination from the environment is not easy. Biodegradation of PAHs is promising since many bacteria have the ability to use hydrocarbons as their sole carbon and energy sources for growth. Of various microorganisms that can degrade PAHs, Pseudomonas aeruginosa is particularly important, not only because it causes a series of diseases including infection in cystic fibrosis patients, but also because it is a model bacterium in various studies. The genes that are responsible for degrading PAHs have been identified in P. aeruginosa, however, no gene acts alone as various stresses often initiate different metabolic pathways, quorum sensing, biofilm formation, antibiotic tolerance, etc. Therefore, it is important to study how PAH degradation genes behave under different conditions. In this study, we apply network analysis to investigating how 46 PAH degradation genes reorganized among 5549 genes in P. aeruginosa PAO1 under nine different conditions using publicly available gene coexpression data from GEO. The results provide six aspects of novelties: (i) comparing the number of gene clusters before and after stresses, (ii) comparing the membership in each gene cluster before and after stresses, (iii) defining which gene changed its membership together with PAH degradation genes before and after stresses, (iv) classifying membership-changed-genes in terms of category in Pseudomonas Genome Database, (v) postulating unknown gene's function, and (vi) proposing new mechanisms for genes of interests. This study can shed light on understanding of cooperative mechanisms of PAH degradation from the level of entire genes in an organism, and paves the way to conduct the similar studies on other genes.

Bottleneck in secretion of α-amylase in Bacillus subtilis.

Amylase plays an important role in biotechnology industries, and Gram-positive bacterium Bacillus subtilis is a major host to produce heterogeneous α-amylases. However, the secretion stress limits the high yield of α-amylase in B. subtilis although huge efforts have been made to address this secretion bottleneck. In this question-oriented review, every effort is made to answer the following questions, which look simple but are long-standing, through reviewing of literature: (1) Does α-amylase need a specific and dedicated chaperone? (2) What signal sequence does CsaA recognize? (3) Does CsaA require ATP for its operation? (4) Does an unfolded α-amylase is less soluble than a folded one? (5) Does α-amylase aggregate before transporting through Sec secretion system? (6) Is α-amylase sufficient stable to prevent itself from misfolding? (7) Does α-amylase need more disulfide bonds to be stabilized? (8) Which secretion system does PrsA pass through? (9) Is PrsA ATP-dependent? (10) Is PrsA reused after folding of α-amylase? (11) What is the fate of PrsA? (12) Is trigger factor (TF) ATP-dependent? The literature review suggests that not only the most of those questions are still open to answers but also it is necessary to calculate ATP budget in order to better understand how B. subtilis uses its energy for production and secretion.

SO2 Emissions in China - Their Network and Hierarchical Structures.

SO2 emissions lead to various harmful effects on environment and human health. The SO2 emission in China has significant contribution to the global SO2 emission, so it is necessary to employ various methods to study SO2 emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO2 emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO2 emissions from power generation sector were highly individualized as small-sized clusters, the SO2 emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO2 emissions from residential sector was not impacted by time, and the SO2 emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO2 emissions from all four sectors and is potentially useful to find out similar patterns of SO2 emissions, which can provide information on understanding the mechanisms of SO2 pollution and on designing different environmental measure to combat SO2 emissions.

Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China.

Specification of PM2.5 spatial and temporal characteristics is important for understanding PM2.5 adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM2.5 emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The results showed that for PM2.5 emissions from industrial sector 8 clusters were found in 2008 but they merged together into a huge cluster in 2010, suggesting that industrial sector underwent an integrating process. For PM2.5 emissions from electricity generation sector, strong locality of clusters was revealed, implying that each region had its own electricity generation system. For PM2.5 emissions from residential sector, the same pattern of 10 clusters was uncovered in both years, implicating the household energy consumption unchanged from 2008 to 2010. For PM2.5 emissions from transportation sector, the same pattern of 5 clusters with many connections in-between was unraveled, indicating the high-speed development of transportation nationalwidely. Except for the known elements, mercury (Hg) surfaced as an element for particle nucleation. To our knowledge, this is the first network study in this field.

Evolutionary evidence on suitability of SecD as a target for development of antibacterial agents against Staphylococcus aureus.

Staphylococcus aureus causes many infections and its drug resistance is a worrying challenge for medical care. The SecD subunit of Sec secretion system in methicillin-resistant S. aureus is an attractive target because SecD dysfunction leads to the death of bacteria and SecD as a target is more efficient than SecA and SecF. Evolution could have made SecD to become insensitive to antibacterial agents although the drugs directly against SecD have yet to develop. So far, no detailed information on SecD evolution has been available, thus 2686 SecD sequences with full taxonomic information from kingdom to species were analyzed. First, the variance of pairwise p-distance was evaluated for each taxonomic group. Second, the variance was further partitioned into intergroup and intragroup variances for quantification of horizontal and vertical gene transfer. Third, phylogenetic tree was built to trace the evolutionary pathway. The results showed that overall evolution of SecDs appears to have undergone horizontal and vertical gene transfer. Only 0.5% horizontal transfers were found between any two SecDs in S. aureus, 6.8% and 8.8% horizontal transfers were found between any two Staphylococcus SecDs from different and the same species, and only one SecD from S. aureus was located far away from its sister cluster. Thus, statistic and evolutionary analyses demonstrate that the SecDs from staphylococcus species have a small chance of mutating, and provide taxonomic evidence to use the SecD as a potential target for new generation of antibacterial agents against S. aureus.

Analysis on evolutionary relationship of amylases from archaea, bacteria and eukaryota.

Amylase is one of the earliest characterized enzymes and has many applications in clinical and industrial settings. In biotechnological industries, the amylase activity is enhanced through modifying amylase structure and through cloning and expressing targeted amylases in different species. It is important to understand how engineered amylases can survive from generation to generation. This study used phylogenetic and statistical approaches to explore general patterns of amylases evolution, including 3118 α-amylases and 280 ß-amylases from archaea, eukaryota and bacteria with fully documented taxonomic lineage. First, the phylogenetic tree was created to analyze the evolution of amylases with focus on individual amylases used in biofuel industry. Second, the average pairwise p-distance was computed for each kingdom, phylum, class, order, family and genus, and its diversity implies multi-time and multi-clan evolution. Finally, the variance was further partitioned into inter-clan variance and intra-clan variance for each taxonomic group, and they represent horizontal and vertical gene transfer. Theoretically, the results show a full picture on the evolution of amylases in manners of vertical and horizontal gene transfer, and multi-time and multi-clan evolution as well. Practically, this study provides the information on the surviving chance of desired amylase in a given taxonomic group, which may potentially enhance the successful rate of cloning and expression of amylase gene in different species.

Predicting Crystallization Propensity of Proteins from Arabidopsis Thaliana.

BACKGROUND: Many studies have correlated characteristics of amino acids with crystallization propensity, as part of the effort to determine the factors that affect the propensity of protein crystallization. However, these characteristics are constant; that is, the encoded amino acid sequences have the same value for each type of amino acid. To overcome this inflexibility, three dynamic characteristics of amino acids and protein were introduced to analyze the crystallization propensity of proteins. Both logistic regression and neural network models were used to correlate each of two dynamic characteristics with the crystallization propensity of 301 proteins from Arabidopsis thaliana, and their results were compared with those obtained from each of 531 constant amino acid characteristics, which served as the benchmark. RESULTS: The neural network model was more powerful for predicting the crystallization propensity of proteins than the logistic regression model. Compared with the benchmark, the dynamic characteristics of amino acids provided good prediction results for the crystallization propensity, and the distribution probability gave the highest sensitivity. Using 90 % accuracy as a cutoff point, the predictable portion of A. thaliana portions was ranked, and the statistical analysis showed that the larger the predictable portion, the better the prediction. CONCLUSIONS: These results demonstrate that dynamic characteristics have a certain relationship with the crystallization propensity, and they could be helpful for the prediction of protein crystallization, which may provide a theoretical concept for certain proteins before conducting experimental crystallization.

Large-scale evolutionary analyses on SecB subunits of bacterial sec system.

Protein secretion systems are extremely important in bacteria because they are involved in many fundamental cellular processes. Of the various secretion systems, the Sec system is composed of seven different subunits in bacteria, and subunit SecB brings secreted preproteins to subunit SecA, which with SecYEG and SecDF forms a complex for the translocation of secreted preproteins through the inner membrane. Because of the wide existence of Sec system across bacteria, eukaryota, and archaea, each subunit of the Sec system has a complicated evolutionary relationship. Until very recently, 5,162 SecB sequences have been documented in UniProtKB, however no phylogenetic study has been conducted on a large sampling of SecBs from bacterial Sec secretion system, and no statistical study has been conducted on such size of SecBs in order to exhaustively investigate their variances of pairwise p-distance along taxonomic lineage from kingdom to phylum, to class, to order, to family, to genus and to organism. To fill in these knowledge gaps, 3,813 bacterial SecB sequences with full taxonomic lineage from kingdom to organism covering 4 phyla, 11 classes, 41 orders, 82 families, 269 genera, and 3,744 organisms were studied. Phylogenetic analysis revealed how the SecBs evolved without compromising their function with examples of 3-D structure comparison of two SecBs from Proteobacteria, and possible factors that affected the SecB evolution were considered. The average pairwise p-distances showed that the variance varied greatly in each taxonomic group. Finally, the variance was further partitioned into inter- and intra-clan variances, which could correspond to vertical and horizontal gene transfers, with relevance for Achromobacter, Brevundimonas, Ochrobactrum, and Pseudoxanthomonas.

Signal peptide of cellulase.

Cellulase is an enzyme playing a crucial role in biotechnology industries ranging from textile to biofuel because of tremendous amount of cellulose produced in plant. In order to improve cellulase productivity, huge resource has been spent in search for good cellulases from microorganism in remote areas and in creation of ideal cellulase by engineering. However, not much attention is given to the secretion of cellulases from cell into extracellular space, where a cellulase plays its enzymatic role. In this minireview, the signal peptides, which lead secreted proteins to specific secretion systems and scatter in literature, are reviewed. The patterns of signal peptides are checked against 4,101 cellulases documented in UniProtKB, the largest protein database in the world, to determine how these cellulases are secreted. Simultaneous review on both literature and cellulases from the database not only provides updated knowledge on signal peptides but also indicates the gap in our research.

Secretory pathway of cellulase: a mini-review.

Cellulase plays an important role in modern industry and holds great potential in biofuel production. Many different types of organisms produce cellulase, which go through secretory pathways to reach the extracellular space, where enzymatic reactions take place. Secretory pathways in various cells have been the focus of many research fields; however, there are few studies on secretory pathways of cellulases in the literature. It is therefore necessary and important to review the current knowledge on the secretory pathways of cellulases. In this mini-review, we address the subcellular locations of cellulases in different organisms, discuss the secretory pathways of cellulases in different organisms, and examine the secretory mechanisms of cellulases. These sections start with a description of general secreted proteins, advance to the situation of cellulases, and end with the knowledge of cellulases, as documented in UniProt Knowledgebase (UniProtKB). Finally, gaps in existing knowledge are highlighted, which may shed light on future studies for biofuel engineering.

Possibility of cross-species/subtype reassortments in influenza A viruses: an analysis of nonstructural protein variations.

The reassortment of genetic segments from different host species and from different subtypes of influenza A viruses occurs frequently, which may generate new strains causing flu epidemic or pandemic. However, the underlined mechanisms of reassortment were less addressed from the viewpoint of protein variations. Recently, we used the amino-acid pair predictability as an indicator to convert eight types of influenza A virus proteins into predictable portion of amino-acid pairs, and then applied the models I and II ANOVA to estimate their differences in terms of subtypes and host species. In order to get a full picture, 2729 and 1063 non-structural 1 and 2 proteins of influenza A viruses were analyzed in this study. The results are consistent with those obtained from hemagglutinin, neuraminidase, nucleoprotein, polymerase acidic protein, polymerase basic proteins 1 and 2, and matrix proteins 1 and 2, indicating that inter-species/subtypes variations are smaller than intra-species/subtype ones. Our findings provide statistical evidence that can partially explains why cross-subtype mutation and cross-species infection easily occur during co-infecting of different strains.

Predictions of enzymatic parameters: a mini-review with focus on enzymes for biofuel.

Enzymatic reactions are very basic processes in biological systems, and parameters related to enzymatic reactions always provide good indicators for understanding of mechanisms underlined in enzymatic reactions, for better controlling of enzymatic reactions, and for comparison of different enzymes. In this mini-review: first, parameters in enzymatic reactions were briefly reviewed from three different standpoints; second, predictions of parameters in enzymatic reactions without information on enzyme structure were shortly reviewed from viewpoints of geometric approach, graphic approach and compartmental approach; third, predictions of parameters in enzymatic reaction with information on enzyme structure were reviewed from the points of view of modeling, with 19 currently available databases, and 17 software packages and web servers; fourth, the current state of prediction on parameters in enzymatic reaction in biofuel industry with respect to cellulolytic enzymes were reviewed; fifth, the pros and cons for future development were discussed; and finally, a worked example was given in the Appendix to describe the whole procedures of prediction of enzymatic parameters in reactions.