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.

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.

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.

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.

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.

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.

Analysis on folding of misgurin using two-dimensional HP model.

Misgurin is an antimicrobial peptide from the loach, while the hydrophobic-polar (HP) model is a way to study the folding conformations and native states in peptide and protein although several amino acids cannot be classified either hydrophobic or polar. Practically, the HP model requires extremely intensive computations, thus it has yet to be used widely. In this study, we use the two-dimensional HP model to analyze all possible folding conformations and native states of misgurin with conversion of natural amino acids according to the normalized amino acid hydrophobicity index as well as the shortest benchmark HP sequence. The results show that the conversion of misgurin into HP sequence with glycine as hydrophobic amino acid at pH 2 has 1212 folding conformations with the same native state of minimal energy -6; the conversion of glycine as polar amino acid at pH 2 has 13,386 folding conformations with three native states of minimal energy -5; the conversion of glycine as hydrophobic amino acid at pH 7 has 2538 folding conformations with three native states of minimal energy -5; and the conversion of glycine as polar amino acid at pH 7 has 12,852 folding conformations with three native states of minimal energy -4. Those native states can be ranked according to the normalized amino acid hydrophobicity index. The detailed discussions suggest two ways to modify misgurin.

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.

Possible impact of global warming on the evolution of hemagglutinins from influenza a viruses.

OBJECTIVE: To determine if global warming has an impact on the evolution of hemagglutinins from influenza A viruses, because both global warming and influenza pandemics/epidemics threaten the world. METHODS: 4 706 hemagglutinins from influenza A viruses sampled from 1956 to 2009 were converted to a time-series to show their evolutionary process and compared with the global, northern hemisphere and southern hemisphere temperatures, to determine if their trends run in similar or opposite directions. Point-to-point comparisons between temperature and quantified hemagglutinins were performed for all species and for the major prevailing species. RESULTS: The comparisons show that the trends for both hemagglutinin evolution and temperature change run in a similar direction. CONCLUSION: Global warming has a consistent and progressive impact on the hemagglutinin evolution of influenza A viruses.

Mutation patterns in human alpha-galactosidase A.

A way to study the mutation pattern is to convert a 20-letter protein sequence into a scalar protein sequence, because the 20-letter protein sequence is neither vector nor scalar while a promising way to study patterns is in numerical domain. In this study, we use the amino-acid pair predictability to convert alpha-galactosidase A with its 137 mutations into scalar sequences, and analyse which amino-acid pairs are more sensitive to mutation. Our results show that the unpredictable amino-acid pairs are more sensitive to mutation, and the mutation trend is to narrow the difference between predicted and actual frequency of amino-acid pairs.

Determination of mutation pattern in human androgen receptor by means of amino-acid pair predictability.

The mutation trend and pattern in protein are currently studied directly using amino acid sequence, however, it would be more efficient if the amino acid sequence is transferred into other domains through quantification because sophisticated mathematical tools can be applied to a numeric sequence. In this study, we apply the amino-acid pair predictability to quantifying the human androgen receptor with its 215 missense point mutations to analyze which amino-acid pairs are sensitive to mutations. The results show that 94.88% mutations occurred at the unpredictable amino-acid pairs, 79% mutations targeted at one or two original amino-acid pairs whose actual frequency is larger than predicted frequency and 63.26% mutations lead to one or two mutated amino-acid pairs with their actual frequency smaller than predicted one.

Connecting KCNQ1 mutants with clinical outcome.

PURPOSE: Mutations in KCNQ1 are linked to long QT and other syndromes. This study reports a method to predict clinical outcome when a mutation at KCNQ1 is found. METHODS: We used amino-acid distribution probability to measure KCNQ1 mutants, and cross-impact analysis to couple KCNQ1 mutants with clinical outcome. Then, Bayesian equation was used to calculate the probability of occurrence of long-QT syndrome in the presence of a mutation. RESULTS: Seventy-six mutations were classified into two groups according to whether a mutation increased or decreased amino-acid distribution probability. Cross-impact analysis showed that a mutation that increases the distribution probability has a greater chance of causing long-QT syndrome than a mutation that decreases the distribution probability. Bayesian calculation suggested that a patient would have a 90% chance of developing long-QT syndrome when a mutation is found at KCNQ1. CONCLUSION: This study details the process of building a quantitative relationship between KCNQ1 mutations and clinical outcome and provides the probability of LQT1 in the presence of a mutation.

Determination of mutation patterns in human ornithine transcarbamylase precursor.

OBJECTIVE: The ornithine transcarbamylase is a mitochondrial matrix homotrimeric enzyme, whose deficiency is the most common genetic defect of the urea cycle and an X-linked semidominant disorder. To understand its mutation pattern is very helpful for managing its clinical manifest and outcome. METHODS: The amino-acid pair predictability is used to transfer the symbolized human ornithine transcarbamylase and its 117 missense point mutants to scalar data and classify the amino-acid pairs as predictable and unpredictable in order that we can analyse the mutation pattern in scalar data domain rather than symbol domain. RESULTS: The results show that the mutation is highly likely to occur at the unpredictable amino-acid pairs, and the mutation has the trend to make an amino-acid pair approach predictable. CONCLUSION: The results provide insight on mutation from the viewpoint based on random mechanism.

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.

Three sampling strategies to predict mutations in H5N1 hemagglutitins from influenza A virus.

After several studies on prediction of mutation, we examine the effect of three sampling strategies, the sampling based on years, the sampling based on number of mutations, and the sampling based on the unpredictable portion of amino-acid pairs, on the prediction performance in H5N1 hemagglutinins. The results show that the sampling strategy does play an important role in prediction, which should be taken into account when predicting the next generation of mutations in proteins from influenza A virus.

Prediction of mutations in H3N2 hemagglutinins of influenza A virus from North America based on different datasets.

With rapid increase in influenza A virus database, an important issue is whether the predictions are similar based on different datasets. Here we stratify 482 H3N2 hemagglutinins from influenza A virus in North America to different datasets. The predictions are made using logistic regression. The results show that the different datasets have significant impact on the predictions.

Building quantitative relationship between changed sequence and changed oxygen affinity in human hemoglobin beta-chain.

244 point mutations have been recorded in human hemoglobin beta-chain, of which some change the oxygen affinity of human hemoglobin beta-chain. We use the amino-acid distribution probability to quantify these mutations, and use the cross-impact analysis with Bayes' law to determine the probability that changes the oxygen affinity of human hemoglobin beta-chain under mutations.