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1.
Molecules ; 27(15)2022 Jul 28.
Article in English | MEDLINE | ID: covidwho-1994116

ABSTRACT

The targeted quantitative NMR (qNMR) approach is a powerful analytical tool, which can be applied to classify and/or determine the authenticity of honey samples. In our study, this technique was used to determine the chemical profiles of different types of Polish honey samples, featured by variable contents of main sugars, free amino acids, and 5-(hydroxymethyl)furfural. One-way analysis of variance (ANOVA) was performed on concentrations of selected compounds to determine significant differences in their levels between all types of honey. For pattern recognition, principal component analysis (PCA) was conducted and good separations between all honey samples were obtained. The results of present studies allow the differentiation of honey samples based on the content of sucrose, glucose, and fructose, as well as amino acids such as tyrosine, phenylalanine, proline, and alanine. Our results indicated that the combination of qNMR with chemometric analysis may serve as a supplementary tool in specifying honeys.


Subject(s)
Honey , Amino Acids/analysis , Animals , Bees , Honey/analysis , Magnetic Resonance Spectroscopy/methods , Poland , Principal Component Analysis
2.
Commun Biol ; 5(1): 651, 2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-1972669

ABSTRACT

Angiotensin-converting enzyme 2 (ACE2) has been identified as a primary receptor for severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2). Here, we investigated the expression regulation of ACE2 in enterocytes under amino acid deprivation conditions. In this study, we found that ACE2 expression was upregulated upon all or single essential amino acid deprivation in human colonic epithelial CCD841 cells. Furthermore, we found that knockdown of general control nonderepressible 2 (GCN2) reduced intestinal ACE2 mRNA and protein levels in vitro and in vivo. Consistently, we revealed two GCN2 inhibitors, GCN2iB and GCN2-IN-1, downregulated ACE2 protein expression in CCD841 cells. Moreover, we found that increased ACE2 expression in response to leucine deprivation was GCN2 dependent. Through RNA-sequencing analysis, we identified two transcription factors, MAFB and MAFF, positively regulated ACE2 expression under leucine deprivation in CCD841 cells. These findings demonstrate that amino acid deficiency increases ACE2 expression and thereby likely aggravates intestinal SARS-CoV-2 infection.


Subject(s)
Amino Acids , Angiotensin-Converting Enzyme 2 , COVID-19 , Enterocytes , Protein Serine-Threonine Kinases , Amino Acids/deficiency , Amino Acids/metabolism , Angiotensin-Converting Enzyme 2/biosynthesis , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/metabolism , COVID-19/enzymology , COVID-19/genetics , COVID-19/virology , Enterocytes/enzymology , Enterocytes/metabolism , Humans , Leucine/pharmacology , Peptidyl-Dipeptidase A/physiology , Protein Serine-Threonine Kinases/antagonists & inhibitors , Protein Serine-Threonine Kinases/genetics , Protein Serine-Threonine Kinases/metabolism , SARS-CoV-2/metabolism
3.
Molecules ; 27(15)2022 Jul 28.
Article in English | MEDLINE | ID: covidwho-1969390

ABSTRACT

The SARS-CoV-2 variant Omicron is characterized, among others, by more than 30 amino acid changes occurring on the spike glycoprotein with respect to the original SARS-CoV-2 spike protein. We report a comprehensive analysis of the effects of the Omicron spike amino acid changes in the interaction with human antibodies, obtained by modeling them into selected publicly available resolved 3D structures of spike-antibody complexes and investigating the effects of these mutations at structural level. We predict that the interactions of Omicron spike with human antibodies can be either negatively or positively affected by amino acid changes, with a predicted total loss of interactions only in a few complexes. Moreover, our analysis applied also to the spike-ACE2 interaction predicts that these amino acid changes may increase Omicron transmissibility. Our approach can be used to better understand SARS-CoV-2 transmissibility, detectability, and epidemiology and represents a model to be adopted also in the case of other variants.


Subject(s)
COVID-19 , SARS-CoV-2 , Amino Acids/genetics , Angiotensin-Converting Enzyme 2 , Humans , Mutation , Peptidyl-Dipeptidase A/metabolism , Spike Glycoprotein, Coronavirus
4.
Microb Biotechnol ; 15(8): 2145-2159, 2022 08.
Article in English | MEDLINE | ID: covidwho-1961453

ABSTRACT

The growing world needs commodity amino acids such as L-glutamate and L-lysine for use as food and feed, and specialty amino acids for dedicated applications. To meet the supply a paradigm shift regarding their production is required. On the one hand, the use of sustainable and cheap raw materials is necessary to sustain low production cost and decrease detrimental effects of sugar-based feedstock on soil health and food security caused by competing uses of crops in the feed and food industries. On the other hand, the biotechnological methods to produce functionalized amino acids need to be developed further, and titres enhanced to become competitive with chemical synthesis methods. In the current review, we present successful strain mutagenesis and rational metabolic engineering examples leading to the construction of recombinant bacterial strains for the production of amino acids such as L-glutamate, L-lysine, L-threonine and their derivatives from methanol as sole carbon source. In addition, the fermentative routes for bioproduction of N-methylated amino acids are highlighted, with focus on three strategies: partial transfer of methylamine catabolism, S-adenosyl-L-methionine dependent alkylation and reductive methylamination of 2-oxoacids.


Subject(s)
Amino Acids , Corynebacterium glutamicum , Amino Acids/metabolism , Corynebacterium glutamicum/genetics , Glutamic Acid/metabolism , Lysine/metabolism , Metabolic Engineering , Methanol/metabolism
5.
PLoS One ; 17(7): e0270276, 2022.
Article in English | MEDLINE | ID: covidwho-1963016

ABSTRACT

SARS-CoV-2 is one of three recognized coronaviruses (CoVs) that have caused epidemics or pandemics in the 21st century and that likely emerged from animal reservoirs. Differences in nucleotide and protein sequence composition within related ß-coronaviruses are often used to better understand CoV evolution, host adaptation, and their emergence as human pathogens. Here we report the comprehensive analysis of amino acid residue changes that have occurred in lineage B ß-coronaviruses that show covariance with each other. This analysis revealed patterns of covariance within conserved viral proteins that potentially define conserved interactions within and between core proteins encoded by SARS-CoV-2 related ß-coronaviruses. We identified not only individual pairs but also networks of amino acid residues that exhibited statistically high frequencies of covariance with each other using an independent pair model followed by a tandem model approach. Using 149 different CoV genomes that vary in their relatedness, we identified networks of unique combinations of alleles that can be incrementally traced genome by genome within different phylogenic lineages. Remarkably, covariant residues and their respective regions most abundantly represented are implicated in the emergence of SARS-CoV-2 and are also enriched in dominant SARS-CoV-2 variants.


Subject(s)
COVID-19 , Evolution, Molecular , SARS-CoV-2 , Amino Acids/genetics , Animals , COVID-19/virology , Genome, Viral , Humans , SARS-CoV-2/genetics
6.
Infect Genet Evol ; 102: 105300, 2022 08.
Article in English | MEDLINE | ID: covidwho-1946053

ABSTRACT

Since the beginning of the Coronavirus disease-2019 pandemic, there has been a growing interest in exploring SARS-CoV-2 genetic variation to understand the origin and spread of the pandemic, improve diagnostic methods and develop the appropriate vaccines. The objective of this study was to identify the SARS-CoV-2s lineages circulating in Tunisia and to explore their amino acid signature in order to follow their genome dynamics. Whole genome sequencing and genetic analyses of fifty-eight SARS-CoV-2 samples collected during one-year between March 2020 and March 2021 from the National Influenza Center were performed using three sampling strategies.. Multiple lineage introductions were noted during the initial phase of the pandemic, including B.4, B.1.1, B.1.428.2, B.1.540 and B.1.1.189. Subsequently, lineages B1.160 (24.2%) and B1.177 (22.4%) were dominant throughout the year. The Alpha variant (B.1.1.7 lineage) was identified in February 2021 and firstly observed in the center of our country. In addition, A clear diversity of lineages was observed in the North of the country. A total of 335 mutations including 10 deletions were found. The SARS-CoV-2 proteins ORF1ab, Spike, ORF3a, and Nucleocapsid were observed as mutation hotspots with a mutation frequency exceeding 20%. The 2 most frequent mutations, D614G in S protein and P314L in Nsp12 appeared simultaneously and are often associated with increased viral infectivity. Interestingly, deletions in coding regions causing consequent deletions of amino acids and frame shifts were identified in NSP3, NSP6, S, E, ORF7a, ORF8 and N proteins. These findings contribute to define the COVID-19 outbreak in Tunisia. Despite the country's limited resources, surveillance of SARS-CoV-2 genomic variation should be continued to control the occurrence of new variants.


Subject(s)
COVID-19 , SARS-CoV-2 , Amino Acids/genetics , COVID-19/epidemiology , Genome, Viral , Humans , Mutation , Phylogeny , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Tunisia/epidemiology
7.
Int J Mol Sci ; 23(12)2022 Jun 15.
Article in English | MEDLINE | ID: covidwho-1911402

ABSTRACT

Many heterologous proteins can be secreted by bacterial ATP-binding cassette (ABC) transporters, provided that they are fused with the C-terminal signal sequence, but some proteins are not secretable even though they carry the right signal sequence. The invention of a method to secrete these non-secretable proteins would be valuable both for understanding the secretory physiology of ABC transporters and for industrial applications. Herein, we postulate that cationic "supercharged" regions within the target substrate protein block the secretion by ABC transporters. We also suggest that the secretion of such substrate proteins can be rescued by neutralizing those cationic supercharged regions via structure-preserving point mutageneses. Surface-protruding, non-structural cationic amino acids within the cationic supercharged regions were replaced by anionic or neutral hydrophilic amino acids, reducing the cationic charge density. The examples of rescued secretions we provide include the spike protein of SARS-CoV-2, glutathione-S-transferase, streptavidin, lipase, tyrosinase, cutinase, growth factors, etc. In summary, our study provides a method to predict the secretability and a tool to rescue the secretion by correcting the secretion-blocking regions, making a significant step in understanding the physiological properties of ABC transporter-dependent protein secretion and laying the foundation for the development of a secretion-based protein-producing platform.


Subject(s)
Bacterial Proteins , COVID-19 , ATP-Binding Cassette Transporters/metabolism , Amino Acids , Bacterial Proteins/metabolism , COVID-19/prevention & control , Humans , Protein Sorting Signals , SARS-CoV-2
8.
J Virol ; 96(14): e0048822, 2022 07 27.
Article in English | MEDLINE | ID: covidwho-1909580

ABSTRACT

Species A rotavirus (RVA) vaccines based on live attenuated viruses are used worldwide in humans. The recent establishment of a reverse genetics system for rotoviruses (RVs) has opened the possibility of engineering chimeric viruses expressing heterologous peptides from other viral or microbial species in order to develop polyvalent vaccines. We tested the feasibility of this concept by two approaches. First, we inserted short SARS-CoV-2 spike peptides into the hypervariable region of the simian RV SA11 strain viral protein (VP) 4. Second, we fused the receptor binding domain (RBD) of the SARS-CoV-2 spike protein, or the shorter receptor binding motif (RBM) nested within the RBD, to the C terminus of nonstructural protein (NSP) 3 of the bovine RV RF strain, with or without an intervening Thosea asigna virus 2A (T2A) peptide. Mutating the hypervariable region of SA11 VP4 impeded viral replication, and for these mutants, no cross-reactivity with spike antibodies was detected. To rescue NSP3 mutants, we established a plasmid-based reverse genetics system for the bovine RV RF strain. Except for the RBD mutant that demonstrated a rescue defect, all NSP3 mutants delivered endpoint infectivity titers and exhibited replication kinetics comparable to that of the wild-type virus. In ELISAs, cell lysates of an NSP3 mutant expressing the RBD peptide showed cross-reactivity with a SARS-CoV-2 RBD antibody. 3D bovine gut enteroids were susceptible to infection by all NSP3 mutants, but cross-reactivity with SARS-CoV-2 RBD antibody was only detected for the RBM mutant. The tolerance of large SARS-CoV-2 peptide insertions at the C terminus of NSP3 in the presence of T2A element highlights the potential of this approach for the development of vaccine vectors targeting multiple enteric pathogens simultaneously. IMPORTANCE We explored the use of rotaviruses (RVs) to express heterologous peptides, using SARS-CoV-2 as an example. Small SARS-CoV-2 peptide insertions (<34 amino acids) into the hypervariable region of the viral protein 4 (VP4) of RV SA11 strain resulted in reduced viral titer and replication, demonstrating a limited tolerance for peptide insertions at this site. To test the RV RF strain for its tolerance for peptide insertions, we constructed a reverse genetics system. NSP3 was C-terminally tagged with SARS-CoV-2 spike peptides of up to 193 amino acids in length. With a T2A-separated 193 amino acid tag on NSP3, there was no significant effect on the viral rescue efficiency, endpoint titer, and replication kinetics. Tagged NSP3 elicited cross-reactivity with SARS-CoV-2 spike antibodies in ELISA. We highlight the potential for development of RV vaccine vectors targeting multiple enteric pathogens simultaneously.


Subject(s)
COVID-19 , Rotavirus , Amino Acids/metabolism , Animals , Antibodies, Viral/metabolism , Cattle , Epitopes/genetics , Epitopes/metabolism , Humans , Reverse Genetics/methods , Rotavirus/metabolism , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/metabolism , Viral Proteins/metabolism
9.
Sci Rep ; 12(1): 6410, 2022 04 19.
Article in English | MEDLINE | ID: covidwho-1908253

ABSTRACT

Coronavirus disease 2019 (COVID-19) is the greatest threat to global health at the present time, and considerable public and private effort is being devoted to fighting this recently emerged disease. Despite the undoubted advances in the development of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, uncertainty remains about their future efficacy and the duration of the immunity induced. It is therefore prudent to continue designing and testing vaccines against this pathogen. In this article we computationally designed two candidate vaccines, one monopeptide and one multipeptide, using a technique involving optimizing lambda-superstrings, which was introduced and developed by our research group. We tested the monopeptide vaccine, thus establishing a proof of concept for the validity of the technique. We synthesized a peptide of 22 amino acids in length, corresponding to one of the candidate vaccines, and prepared a dendritic cell (DC) vaccine vector loaded with the 22 amino acids SARS-CoV-2 peptide (positions 50-71) contained in the NTD domain (DC-CoVPSA) of the Spike protein. Next, we tested the immunogenicity, the type of immune response elicited, and the cytokine profile induced by the vaccine, using a non-related bacterial peptide as negative control. Our results indicated that the CoVPSA peptide of the Spike protein elicits noticeable immunogenicity in vivo using a DC vaccine vector and remarkable cellular and humoral immune responses. This DC vaccine vector loaded with the NTD peptide of the Spike protein elicited a predominant Th1-Th17 cytokine profile, indicative of an effective anti-viral response. Finally, we performed a proof of concept experiment in humans that included the following groups: asymptomatic non-active COVID-19 patients, vaccinated volunteers, and control donors that tested negative for SARS-CoV-2. The positive control was the current receptor binding domain epitope of COVID-19 RNA-vaccines. We successfully developed a vaccine candidate technique involving optimizing lambda-superstrings and provided proof of concept in human subjects. We conclude that it is a valid method to decipher the best epitopes of the Spike protein of SARS-CoV-2 to prepare peptide-based vaccines for different vector platforms, including DC vaccines.


Subject(s)
COVID-19 Vaccines , COVID-19 , Amino Acids , COVID-19/prevention & control , Cytokines , Epitopes , Humans , Immunogenicity, Vaccine , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/chemistry , Vaccines, Subunit
10.
Front Cell Infect Microbiol ; 12: 748948, 2022.
Article in English | MEDLINE | ID: covidwho-1902922

ABSTRACT

Viruses rapidly co-evolve with their hosts. The 9 million sequenced SARS-CoV-2 genomes by March 2022 provide a detailed account of viral evolution, showing that all amino acids have been mutated many times. However, only a few became prominent in the viral population. Here, we investigated the emergence of the same mutations in unrelated parallel lineages and the extent of such convergent evolution on the molecular level in the spike (S) protein. We found that during the first phase of the pandemic (until mid 2021, before mass vaccination) 31 mutations evolved independently ≥3-times within separated lineages. These included all the key mutations in SARS-CoV-2 variants of concern (VOC) at that time, indicating their fundamental adaptive advantage. The omicron added many more mutations not frequently seen before, which can be attributed to the synergistic nature of these mutations, which is more difficult to evolve. The great majority (24/31) of S-protein mutations under convergent evolution tightly cluster in three functional domains; N-terminal domain, receptor-binding domain, and Furin cleavage site. Furthermore, among the S-protein receptor-binding motif mutations, ACE2 affinity-improving substitutions are favoured. Next, we determined the mutation space in the S protein that has been covered by SARS-CoV-2. We found that all amino acids that are reachable by single nucleotide changes have been probed multiple times in early 2021. The substitutions requiring two nucleotide changes have recently (late 2021) gained momentum and their numbers are increasing rapidly. These provide a large mutation landscape for SARS-CoV-2 future evolution, on which research should focus now.


Subject(s)
SARS-CoV-2 , Spike Glycoprotein, Coronavirus , Amino Acids , Mutation , Nucleotides , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics
11.
Cells ; 11(12)2022 06 14.
Article in English | MEDLINE | ID: covidwho-1896810

ABSTRACT

The new coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has been reported and spread globally. There is an urgent need to take urgent measures to treat and prevent further infection of this virus. Here, we use virtual drug screening to establish pharmacophore groups and analyze the ACE2 binding site of the spike protein with the ZINC drug database and DrugBank database by molecular docking and molecular dynamics simulations. Screening results showed that Venetoclax, a treatment drug for chronic lymphocytic leukemia, has a potential ability to bind to the spike protein of SARS-CoV-2. In addition, our in vitro study found that Venetoclax degraded the expression of the spike protein of SARS-CoV-2 through amino acids Q493 and S494 and blocked the interaction with the ACE2 receptor. Our results suggest that Venetoclax is a candidate for clinical prevention and treatment and deserves further research.


Subject(s)
COVID-19 , SARS-CoV-2 , Amino Acids/metabolism , Angiotensin-Converting Enzyme 2 , Bridged Bicyclo Compounds, Heterocyclic , COVID-19/drug therapy , Humans , Molecular Docking Simulation , Peptidyl-Dipeptidase A/metabolism , Protein Binding , Spike Glycoprotein, Coronavirus/chemistry , Sulfonamides
12.
Biomed Environ Sci ; 35(5): 393-401, 2022 May 20.
Article in English | MEDLINE | ID: covidwho-1893035

ABSTRACT

Objective: The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been engendering enormous hazards to the world. We obtained the complete genome sequences of SARS-CoV-2 from imported cases admitted to the Guangzhou Eighth People's Hospital, which was appointed by the Guangdong provincial government to treat coronavirus disease 2019 (COVID-19). The SARS-CoV-2 diversity was analyzed, and the mutation characteristics, time, and regional trend of variant emergence were evaluated. Methods: In total, 177 throat swab samples were obtained from COVID-19 patients (from October 2020 to May 2021). High-throughput sequencing technology was used to detect the viral sequences of patients infected with SARS-CoV-2. Phylogenetic and molecular evolutionary analyses were used to evaluate the mutation characteristics and the time and regional trends of variants. Results: We observed that the imported cases mainly occurred after January 2021, peaking in May 2021, with the highest proportion observed from cases originating from the United States. The main lineages were found in Europe, Africa, and North America, and B.1.1.7 and B.1.351 were the two major sublineages. Sublineage B.1.618 was the Asian lineage (Indian) found in this study, and B.1.1.228 was not included in the lineage list of the Pangolin web. A reasonably high homology was observed among all samples. The total frequency of mutations showed that the open reading frame 1a (ORF1a) protein had the highest mutation density at the nucleotide level, and the D614G mutation in the spike protein was the commonest at the amino acid level. Most importantly, we identified some amino acid mutations in positions S, ORF7b, and ORF9b, and they have neither been reported on the Global Initiative of Sharing All Influenza Data nor published in PubMed among all missense mutations. Conclusion: These results suggested the diversity of lineages and sublineages and the high homology at the amino acid level among imported cases infected with SARS-CoV-2 in Guangdong Province, China.


Subject(s)
COVID-19 , SARS-CoV-2 , Amino Acids , COVID-19/epidemiology , Genomics , Humans , Mutation , Phylogeny , SARS-CoV-2/genetics
13.
J Proteome Res ; 21(7): 1640-1653, 2022 07 01.
Article in English | MEDLINE | ID: covidwho-1890103

ABSTRACT

The coronavirus disease 2019 (Covid-19), which caused respiratory problems in many patients worldwide, led to more than 5 million deaths by the end of 2021. Experienced symptoms vary from mild to severe illness. Understanding the infection severity to reach a better prognosis could be useful to the clinics, and one study area to fulfill one piece of this biological puzzle is metabolomics. The metabolite profile and/or levels being monitored can help predict phenotype properties. Therefore, this study evaluated plasma metabolomes of 110 individual samples, 57 from control patients and 53 from recent positive cases of Covid-19 (IgM 98% reagent), representing mild to severe symptoms, before any clinical intervention. Polar metabolites from plasma samples were analyzed by quantitative 1H NMR. Glycerol, 3-aminoisobutyrate, formate, and glucuronate levels showed alterations in Covid-19 patients compared to those in the control group (Tukey's HSD p-value cutoff = 0.05), affecting the lactate, phenylalanine, tyrosine, and tryptophan biosynthesis and d-glutamine, d-glutamate, and glycerolipid metabolisms. These metabolic alterations show that SARS-CoV-2 infection led to disturbance in the energetic system, supporting the viral replication and corroborating with the severe clinical conditions of patients. Six polar metabolites (glycerol, acetate, 3-aminoisobutyrate, formate, glucuronate, and lactate) were revealed by PLS-DA and predicted by ROC curves and ANOVA to be potential prognostic metabolite panels for Covid-19 and considered clinically relevant for predicting infection severity due to their straight roles in the lipid and energy metabolism. Thus, metabolomics from samples of Covid-19 patients is a powerful tool for a better understanding of the disease mechanism of action and metabolic consequences of the infection in the human body and may corroborate allowing clinicians to intervene quickly according to the needs of Covid-19 patients.


Subject(s)
COVID-19 , Amino Acids , COVID-19/diagnosis , Formates , Glucuronates , Glycerol , Humans , Lactates , Metabolomics , SARS-CoV-2
14.
Int J Mol Sci ; 23(12)2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1884213

ABSTRACT

Monitoring SARS-CoV-2's genetic diversity and emerging mutations in this ongoing pandemic is crucial to understanding its evolution and ensuring the performance of COVID-19 diagnostic tests, vaccines, and therapies. Spain has been one of the main epicenters of COVID-19, reaching the highest number of cases and deaths per 100,000 population in Europe at the beginning of the pandemic. This study aims to investigate the epidemiology of SARS-CoV-2 in Spain and its 18 Autonomous Communities across the six epidemic waves established from February 2020 to January 2022. We report on the circulating SARS-CoV-2 variants in each epidemic wave and Spanish region and analyze the mutation frequency, amino acid (aa) conservation, and most frequent aa changes across each structural/non-structural/accessory viral protein among the Spanish sequences deposited in the GISAID database during the study period. The overall SARS-CoV-2 mutation frequency was 1.24 × 10-5. The aa conservation was >99% in the three types of protein, being non-structural the most conserved. Accessory proteins had more variable positions, while structural proteins presented more aa changes per sequence. Six main lineages spread successfully in Spain from 2020 to 2022. The presented data provide an insight into the SARS-CoV-2 circulation and genetic variability in Spain during the first two years of the pandemic.


Subject(s)
COVID-19 , Pandemics , Amino Acids/genetics , COVID-19/epidemiology , COVID-19/genetics , Genome, Viral , Humans , Mutation , Phylogeny , SARS-CoV-2/genetics , Spain/epidemiology
15.
Infect Dis Poverty ; 11(1): 50, 2022 May 04.
Article in English | MEDLINE | ID: covidwho-1883543

ABSTRACT

BACKGROUND: Influenza B virus can cause epidemics with high pathogenicity, so it poses a serious threat to public health. A feature representation algorithm is proposed in this paper to identify the pathogenicity phenotype of influenza B virus. METHODS: The dataset included all 11 influenza virus proteins encoded in eight genome segments of 1724 strains. Two types of features were hierarchically used to build the prediction model. Amino acid features were directly delivered from 67 feature descriptors and input into the random forest classifier to output informative features about the class label and probabilistic prediction. The sequential forward search strategy was used to optimize the informative features. The final features for each strain had low dimensions and included knowledge from different perspectives, which were used to build the machine learning model for pathogenicity identification. RESULTS: The 40 signature positions were achieved by entropy screening. Mutations at position 135 of the hemagglutinin protein had the highest entropy value (1.06). After the informative features were directly generated from the 67 random forest models, the dimensions for class and probabilistic features were optimized as 4 and 3, respectively. The optimal class features had a maximum accuracy of 94.2% and a maximum Matthews correlation coefficient of 88.4%, while the optimal probabilistic features had a maximum accuracy of 94.1% and a maximum Matthews correlation coefficient of 88.2%. The optimized features outperformed the original informative features and amino acid features from individual descriptors. The sequential forward search strategy had better performance than the classical ensemble method. CONCLUSIONS: The optimized informative features had the best performance and were used to build a predictive model so as to identify the phenotype of influenza B virus with high pathogenicity and provide early risk warning for disease control.


Subject(s)
Amino Acids , Influenza B virus , Algorithms , Amino Acids/genetics , Influenza B virus/genetics , Machine Learning , Virulence
16.
Genes (Basel) ; 13(5)2022 Apr 25.
Article in English | MEDLINE | ID: covidwho-1875529

ABSTRACT

Homorepeat sequences, consecutive runs of identical amino acids, are prevalent in eukaryotic proteins. It has become necessary to annotate and evaluate this feature in entire proteomes. The definition of what constitutes a homorepeat is not fixed, and different research approaches may require different definitions; therefore, flexible approaches to analyze homorepeats in complete proteomes are needed. Here, we present polyX2, a fast, simple but tunable script to scan protein datasets for all possible homorepeats. The user can modify the length of the window to scan, the minimum number of identical residues that must be found in the window, and the types of homorepeats to be found.


Subject(s)
Eukaryota , Proteome , Amino Acids , Eukaryotic Cells , Proteome/chemistry , Proteome/genetics , Repetitive Sequences, Amino Acid
17.
BMC Genomics ; 23(1): 377, 2022 May 18.
Article in English | MEDLINE | ID: covidwho-1849668

ABSTRACT

BACKGROUND: In the pursuit of a better understanding of biodiversity, evolutionary biologists rely on the study of phylogenetic relationships to illustrate the course of evolution. The relationships among natural organisms, depicted in the shape of phylogenetic trees, not only help to understand evolutionary history but also have a wide range of additional applications in science. One of the most challenging problems that arise when building phylogenetic trees is the presence of missing biological data. More specifically, the possibility of inferring wrong phylogenetic trees increases proportionally to the amount of missing values in the input data. Although there are methods proposed to deal with this issue, their applicability and accuracy is often restricted by different constraints. RESULTS: We propose a framework, called PhyloMissForest, to impute missing entries in phylogenetic distance matrices and infer accurate evolutionary relationships. PhyloMissForest is built upon a random forest structure that infers the missing entries of the input data, based on the known parts of it. PhyloMissForest contributes with a robust and configurable framework that incorporates multiple search strategies and machine learning, complemented by phylogenetic techniques, to provide a more accurate inference of lost phylogenetic distances. We evaluate our framework by examining three real-world datasets, two DNA-based sequence alignments and one containing amino acid data, and two additional instances with simulated DNA data. Moreover, we follow a design of experiments methodology to define the hyperparameter values of our algorithm, which is a concise method, preferable in comparison to the well-known exhaustive parameters search. By varying the percentages of missing data from 5% to 60%, we generally outperform the state-of-the-art alternative imputation techniques in the tests conducted on real DNA data. In addition, significant improvements in execution time are observed for the amino acid instance. The results observed on simulated data also denote the attainment of improved imputations when dealing with large percentages of missing data. CONCLUSIONS: By merging multiple search strategies, machine learning, and phylogenetic techniques, PhyloMissForest provides a highly customizable and robust framework for phylogenetic missing data imputation, with significant topological accuracy and effective speedups over the state of the art.


Subject(s)
Algorithms , DNA , Amino Acids , Phylogeny , Sequence Alignment
18.
Viruses ; 14(4)2022 03 30.
Article in English | MEDLINE | ID: covidwho-1834926

ABSTRACT

The H9N2 subtype avian influenza viruses (AIVs) have been circulating in China for more than 20 years, attracting more and more attention due to the potential threat of them. At present, vaccination is a common prevention and control strategy in poultry farms, but as virus antigenicity evolves, the immune protection efficiency of vaccines has constantly been challenged. In this study, we downloaded the hemagglutinin (HA) protein sequences of the H9N2 subtype AIVs from 1994 to 2019 in China-with a total of 5138 sequences. The above sequences were analyzed in terms of time and space, and it was found that h9.4.2.5 was the most popular in various regions of China. Furthermore, the prevalence of H9N2 subtype AIVs in China around 2006 was different. The domestic epidemic branch was relatively diversified from 1994 to 2006. After 2006, the epidemic branch each year was h9.4.2.5. We compared the sequences around 2006 as a whole and screened out 15 different amino acid positions. Based on the HA protein of A/chicken/Guangxi/55/2005 (GX55), the abovementioned amino acid mutations were completed. According to the 12-plasmid reverse genetic system, the rescue of the mutant virus was completed using A/PuertoRico/8/1934 (H1N1) (PR8) as the backbone. The cross hemagglutination inhibition test showed that these mutant sites could transform the parental strain from the old to the new antigenic region. Animal experiments indicated that the mutant virus provided significant protection against the virus from the new antigenic region. This study revealed the antigenic evolution of H9N2 subtype AIVs in China. At the same time, it provided an experimental basis for the development of new vaccines.


Subject(s)
Influenza A Virus, H1N1 Subtype , Influenza A Virus, H9N2 Subtype , Influenza in Birds , Amino Acids/genetics , Animals , Chickens , China/epidemiology , Evolution, Molecular , Hemagglutinin Glycoproteins, Influenza Virus/genetics , Hemagglutinins/genetics , Influenza A Virus, H9N2 Subtype/genetics , Phylogeny
19.
Brief Bioinform ; 23(3)2022 05 13.
Article in English | MEDLINE | ID: covidwho-1831016

ABSTRACT

Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The best prediction models have been tested on independent validation datasets, which achieved an overall Matthews correlation coefficient value of 0.87 for animal-pathogen using the combined pseudo-amino acid composition and conjoint triad (PAAC_CT) features, 0.75 for human-bacteria using the combined pseudo-amino acid composition, conjoint triad and normalized Moreau-Broto feature (PAAC_CT_NMBroto), 0.96 for human-virus using PAAC_CT_NMBroto and 0.94 values for plant-pathogen interactions using the combined pseudo-amino acid composition, composition and transition feature (PAAC_CTDC_CTDT). Our server running deepHPI is deployed on a high-performance computing cluster that enables large and multiple user requests, and it provides more information about interactions discovered. It presents an enriched visualization of the resulting host-pathogen networks that is augmented with external links to various protein annotation resources. We believe that the deepHPI web server will be very useful to researchers, particularly those working on infectious diseases. Additionally, many novel and known host-pathogen systems can be further investigated to significantly advance our understanding of complex disease-causing agents. The developed models are established on a web server, which is freely accessible at http://bioinfo.usu.edu/deepHPI/.


Subject(s)
COVID-19 , Communicable Diseases , Deep Learning , Amino Acids , Animals , Host-Pathogen Interactions , Machine Learning
20.
J Phys Condens Matter ; 34(29)2022 05 18.
Article in English | MEDLINE | ID: covidwho-1830918

ABSTRACT

Herein, we report a computational investigation of the binding affinity of dexamethasone, betamethasone, chloroquine and hydroxychloroquine to SARS-CoV-2 main protease using molecular and quantum mechanics as well as molecular docking methodologies. We aim to provide information on the anti-COVID-19 mechanism of the abovementioned potential drugs against SARS-CoV-2 coronavirus. Hence, the 6w63 structure of the SARS-CoV-2 main protease was selected as potential target site for the docking analysis. The study includes an initial conformational analysis of dexamethasone, betamethasone, chloroquine and hydroxychloroquine. For the most stable conformers, a spectroscopic analysis has been carried out. In addition, global and local reactivity indexes have been calculated to predict the chemical reactivity of these molecules. The molecular docking results indicate that dexamethasone and betamethasone have a higher affinity than chloroquine and hydroxychloroquine for their theoretical 6w63 target. Additionally, dexamethasone and betamethasone show a hydrogen bond with the His41 residue of the 6w63 protein, while the interaction between chloroquine and hydroxychloroquine with this amino acid is weak. Thus, we confirm the importance of His41 amino acid as a target to inhibit the SARS-CoV-2 Mpro activity.


Subject(s)
COVID-19 , SARS-CoV-2 , Amino Acids , Betamethasone , COVID-19/drug therapy , Chloroquine/chemistry , Chloroquine/pharmacology , Coronavirus 3C Proteases , Dexamethasone/pharmacology , Humans , Hydroxychloroquine/chemistry , Hydroxychloroquine/pharmacology , Molecular Docking Simulation , Molecular Dynamics Simulation , Protease Inhibitors/pharmacology
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