Cloning, expression and identification of truncated spike gene of IBV (Sf200) and chicken granulocyte- macrophage colony stimulating factor (GM-CSF)

The aim of this study was to clone, express and identify the truncated S1 gene of nephrotropic infectious bronchitis virus (IBV) and granulocyte-monocyte colony stimulating factor (GM-CSF) of chicken. Firstly, two genes were amplified by polymerase chain reaction (PCR) and cloned into pMD18-T vector. The truncated S1 gene designated as Sf200 containing five antigenic sites of S1 glycoprotein on amino acid residues (aa) 24-61, (aa) 291-398 and (aa) 497-543 and GM-CSF were then amplified from the respective recombinant pMD18-T plasmids and cloned into pET-32a (+) vector resulting pET-Sf200, pET-GM which were identified by restriction enzyme digestion and sequencing analysis. The in vitro expression of truncated Sf200 and GM-CSF constructs were later expressed in E. coli BL21 with a molecular mass of approximately 38 kDa and 29 kDa respectively as judged by sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis. Polyclonal antibodies were developed by injecting E. coli expressed Sf200 and GM-CSF into the SPF mice and were used to identify the recombinant proteins by Western blot analysis. These findings indicated that the polyclonal antibodies produced in mice could be used to detect the recombinant truncated Sf200 and GM-CSF and vice versa.

Isolation, identification and genetic evolution analysis of porcine epidemic diarrhea virus SNJ-P strain in Sichuan Province

Purpose: To investigate the epidemic variation of porcine epidemic diarrhea virus (PEDV) strains in Sichuan Province, and to analyze the causes of poor vaccination effect. Methods: Piglet intestinal samples were collected from a pig farm in Sichuan Province for PCR detection, virus purification, determination of virus titer and virus infection experiments. Whole genome sequencing of isolated strains was determined. The S gene sequence of the isolated strain was compared with the strains from other regions and vaccine strains, and the phylogenetic tree was established. The amino acid site variation of S protein between the isolated strain and the classical vaccine strain CV777 was compared. Results: A PEDV strain was successfully isolated and named as PEDV SNJ-P. The determination of virus titer was 1..107.5/100 L. Animal infection experiments showed that the isolated strain could cause diarrhea, dehydration and other symptoms and lead to death in piglets. Genome sequencing and phylogenetic tree analysis showed that the whole gene of PEDV SNJ-P strain was 28003 bp, and the genotype of the strain was S non-INDEL type. The strains were closely related to the strains of PEDV-WS, CH/JLDH/2016 and CH/HNLH/2015 isolated from China, and were relatively distant with the same type vaccine strain, and were far from the classical vaccine strain. Compared with the classical vaccine strain CV777, the S protein of SNJ-P strain had multiple amino acid mutations, deletions and insertions. Conclusion: Due to the continuous variation of strains, SNJ-P strain is far from the vaccine strain, and the current vaccines cannot provide effective protection. The results of this study are expected to provide reference for the study of PEDV strains and vaccine development in China.

Complete genome sequencing and molecular genetic evolution analysis of Guangxi mutant of porcine epidemic diarrhea virus

In order to understand the genomic characteristics and molecular genetic diversity of porcine epidemic diarrhea virus(PEDV) in Guangxi in recent years, 11 pairs of specific primers were designed to detect the whole genome of PEDV GXNN isolated from porcine diarrhea in Nanning, Guangxi, China, and similarity comparison, genetic evolution, gene variation and S gene recombination were also analyzed. The results showed that full length of the GXNN strain was 28 035 bp, had similar genomic characteristics with other PEDV isolates, about 96.4%-98.7% nucleotide similarity with different reference strains, and the nucleotide similarity of S, ORF3, M and N genes was 93.7%-98.9%, 90.9%-99.4%, 97.4%-99.7% and 95.6%-99.2%;the amino acid similarity of them was 92.9%-99.5%, 91.3%-99.1%, 97.4%-99.1% and 96.4%-99.5%. GXNN is closely related to most domestic isolates in recent years. Phylogenetic tree showed that GXNN closely related to most strains isolated in China recent years, belonged to GII-b subtype. However, it was low relatedness to classic vaccine strains, domestic early epidemic strains, foreign epidemic strains and Guangxi CH-GX-2015-750 A, they belong to different subtypes. Compared with the 5 vaccine strains, the S gene of GXNN stain has a large variation, by inserting amino acid Q at positions 118 844 and 905 sizes, four unique amino acid mutations in the core neutralizing epitope(COE)region and the main epitope region, and 14 mutations in other regions. 126 T/A, 199 A/V and 103 T/A site mutations of ORF3, M and N genes were happened at position 126, 4 D4 region and PN-D4 region, respectively. Recombination analysis revealed that there was a potential recombination region in the hypervariable region of S gene at 826-3 142 nt. This study successfully obtained the complete genome sequence of a PEDV strain, and analyzed its genetic variation and provided a reference for PEDV molecular epidemiology research and new vaccine development.

Spike2Signal: Classifying Coronavirus Spike Sequences with Deep Learning

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the COVID-19 disease in humans, which has reached the scale of a global pandemic. Changes in the composition of the genome of the virus, in the form of mutations, can alter its ability to infect host cells. These modified forms of the virus are known as variants. The spike region of the SARS-CoV-2 genome has a crown-like structure - where 'coronavirus' gets its name. In SARS-CoV-2, it has been noted that mutations happen disproportionately many in the spike region, making this region important for distinguishing different variants. Since amino acids (of the spike protein sequence) are not in a numerical form, they are of no direct use to machine learning algorithms. Thus we use various embedding techniques to make such spike sequence data amenable to machine learning approaches. However, there is ongoing research to find better solutions to study these variants using classification. This paper presents a transformation for spike sequences, called Spike2Signal, to allow the classification of different variants of SARS-CoV-2 using deep learning algorithms. Spike2Signal converts spike sequences into a signal-like representation to allow the classification by state-of-the-art time-series classifiers. Further, we transform this Spike2Signal representation into an image (Spike2Image) to allow the usage of state-of-the-art image classifiers and compare these results with those obtained purely with Spike2Signal. In a wider comparison with existing feature engineering-based methods, we show that the Spike2Signal representation allows to outperform all baselines in predictive power. © 2022 IEEE.

Bioinformatics analysis of mutations in the spike protein of SARS-CoV-2 and their effects

In December 2019, a new type of pneumonia, coronavirus disease 2019 (COVID-19), caused by a novel coronavirus, SARS-CoV-2, was detected in hospitals in Wuhan, Hubei Province, China. The World Health Organization announced on 11 March 2020 that COVID-19 can be characterized as a pandemic, and since then COVID-19 has wrought havOc on public-health systems worldwide. The surface "spike" protein CS protein of SARS-CoV-2 mediates host-cell attachment and membrane fusion. The S protein is a key target for urgent development of vaccines, therapeutic antibodies, and diagnostics. To analyze the mutations and their effects on protein structure and function of the S protein, bioinformatics software has been used to analyze its nucleotide and amino-acid sequences, and Wuhan-Hu-1 (GenBank accession number: MN908947.3) was used as standard strain. As of 17 April 2020, there were 1, 002 SARS-CoV-2 strains in the GenBank database, of which 12 strains had mutations in the amino-acid sequence of the S protein. Some of these mutations could affect the physicochemical properties and secondary structures of the S protein. The R4081 mutation was located in the receptor-binding domain (RBD) and displayed on the surface, and could affect the RBD structure. The mutated amino acids 48, 74, 181, 221 and 655 were located in predicted linear epitopes of B cells, and 74, 181 and 655 mutations could greatly affect the structures and properties of linear epitopes of B cells.. The S protein of SARS-CoV-2 isolated from humans, dogs, cats and lions was highly conserved, whereas the D614G mutation was found in the isolated strain from tigers. Furthermore, the unique Flynn protease recognition site was presented in the S protein of SARS-CoV-2 compared with the coronavirus from bats. These results suggest that the S protein of SARS-CoV-2 is relatively conserved within and between species, whereas there are some mutations that can affect the physicochemical properties and structures of the S protein, which may also affect the linear epitopes of B cells. Taken together, these data provide a basis for the research and development of drugs, antibodies and vaccines against SARS-CoV-2.

Glycoinformatics approach for identifying target positions to inhibit initial binding of SARS-CoV-2 S1 protein to the host cell

COVID-19 outbreak is still threatening the public health. Therefore, in the middle of the pandemic, all kind of knowledge on SARS-CoV-2 may help us to find the solution. Determining the 3D structures of the proteins involved in host-pathogen interactions are of great importance in the fight against infection. Besides, post-translational modifications of the protein on 3D structure should be revealed in order to understand the protein function since these modifications are responsible for the host-pathogen interaction. Based on these, we predicted O-glycosylation and phosphorylation positions using full amino acid sequence of S1 protein. Candidate positions were further analyzed with enzyme binding activity, solvent accessibility, surface area parameters and the positions determined with high accuracy rate were used to design 3D O-glycoprotein structure of the S1 protein using carbohydrate force field. In addition, the interaction between the C-type lectin CD209L and a-mannose residues was examined and carbohydrate recognition positions were predicted. We suggest these positions as a potential target for the inhibition of the initial binding of SARS-CoV-2 S1 protein to the host cell.

Evaluation of some plant-derived natural ingredients against SARS-CoV-2: an in-silico approach

Background: The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infected by a new strain of human coronavirus, has engulfed the whole globe with its vicious potential to eradicate humankind. The pandemic has emerged from the Wuhan provinces of China with high transmissibility. Researchers are rushing to discover vaccines and drugs for the disease, which is not known yet. In this study, we have focused on the in-silico screening of phytochemicals occurring naturally in plant extracts that could possibly interact with receptor binding motif (RBM) of spike protein and thereby inhibit virus-cell interaction. Materials and Methods: In this study, we have taken 100 phytochemicals that have been studied in various viral interactions and have shown antiviral properties. Initially, these compounds were analyzed on the basis of their physicochemical and pharmacokinetic properties, biological activities, possible target interactions, similar compounds in humans, and gene regulations using bioinformatic tools, namely Swiss-ADME, PASS (prediction of activity spectra for substances), SwissTargetPrediction, similar ensemble approach (SEA) search server, DIEGP-pred, respectively and were filtered out on the basis of immunobiological activities and expression of genes involved in cytokine storm regulation and immunostimulation. Further, they were docked with the receptor-binding domain (RBD) of spike protein in the SARS-CoV-2 using SwissDock and analyzed by UCSF Chimera.

Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review.

SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging-including CT and X-ray and how they can be processed-and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe.

Bioinformatics analysis of structure and function of S protein of SARS-CoV-2

It has been found in previous studies that S protein of coronavirus plays a decisive role in invading host cells. In this paper, nucleotide sequence of SARS-CoV-2 S gene and its encoding amino acid sequence were obtained from NCBI. The bioinformatics analysis of S protein was carried out by DNAMAN, DNAStar, Mega 7.0 and some online analysis and prediction websites. The S protein of SARS-CoV-2 and SARS-CoV has high homology, which suggests that the structure and function of S protein of the two strains are similar. S protein contains signal peptide sequence, and the number of potential phosphorylated amino acids is 136, and the other amino acids have high phosphorylation tendency;there are 17 N-glycosylation sites, and it is a membrane protein, containing S1 and S2 domains;in the prediction results of secondary structure, a helix accounted for 28.59%, beta turn accounted for 3.38%, extended strand accounted for 23.25%, and random coil accounted for 44.78%. The quaternary structure shows that the complete S protein is composed of three monomers. This study can provide a theoretical basis for the infection mechanism and specific treatment of SARS-CoV-2.

A Novel Scalable Feature Extraction Approach for COVID-19 Protein Sequences and their Cluster Analysis with Kernelized Fuzzy Algorithm

COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization on March 11, 2020. To solve the global problem of analysis of different variants of COVID-19 genome sequences, there is a need to develop intel-ligent, scalable machine learning techniques that can process and analyze important COVID-19 protein data by utilizing the Big Data framework. For this, we have first proposed a feature extraction approach for COVID-19 protein data named Scalable Distributed Co-occurrence-based Probability-Specific Feature extraction approach (SDCPSF). The proposed SDCPSF approach is executed on the Apache Spark cluster to preprocess the massive COVID-19 protein sequences. The proposed SDCPSF represents each variable-length COVID-19 protein sequence with fixed length six dimensions numeric feature vectors. Then the extracted features are used as input to the kernelized fuzzy clustering algorithms, i.e., KSRSIO-FCM and KSLFCM, which efficiently performs clustering of big data due to its in-memory cluster computing technique and thus forms clusters of COVID-19 genome sequences. Furthermore, the performance of KSRSIO-FCM is compared with another scalable clustering algorithm, i.e., KSLFCM, in terms of the Silhouette index (SI) and Davies-Bouldin index (DBI). © 2022 IEEE.

SARS-CoV-2 variants and the so-called resistance to vaccines

RNA viruses (except retroviruses) replicate by the action of an RNA-dependent RNA polymerase, which lacks a proofreading exonuclease and, consequently, errors may occur in each replication giving place to viral mutants. Depending on their fitness, these mutants either become extinct or thrive, spawning variants that escape the immune system. The most important SARS-CoV-2 mutations are those that alter the amino acid sequence in the viral S protein because this protein holds the key for the virus to enter the human cell. The more viruses replicate, the more they mutate, and the more likely it is that dominant resistant variants will appear. In such cases, more stringent measures for community protection will be required. Vaccines and polyclonal antibodies, which induce a response directed towards several sites along the S protein, would maintain effective protection against SARS-CoV-2 variants. Furthermore, vaccines appear to induce an increased helper and cytotoxic T-cell response, which may also be a biomarker of protection. In densely populated areas with insufficient protection measures, the virus spreads freely, thus increasing the likelihood of generating escape mutants. India and Manaus exemplify this situation. Natural evolution selects the mutants that multiply most efficiently without eliminating the host, thus facilitating their spread. Contrastingly, the circulation of viruses of high virulence and lethality (Ebola, hantavirus) that eliminate the host remain limited to certain geographic areas, without further dissemination. Therefore, it would be expected that SARS-CoV-2 will evolve into more infectious and less virulent variants.

Is the SARS-cov2 evolved in human being: a prospective genetic analysis

COVID-19 is the deadly respiratory disease of the century caused by new type unknown origin Coronavirus. The recent effort of the word researchers is toward finding the origin of the virus. The current study investigated the extent of molecular similarity and divergence between SARS-CoV2 and other related Coronavirus. An attempt has been made to investigate the epidemiological study of this new contagious virus using molecular biology techniques. The phylogenetic trees for all human coronaviruses with the novel Coronavirus have been built using a several complete amino acid sequences of the four known structural proteins, S (spike), E (envelope), M (membrane), and N (nucleocapsid). The result of the study revealed that the SARS-CoV2 is related to human SARS-CoV isolated from different countries very cloely, especially those strains recovered from China in recent times, 2020. The evolutionary changes observed in the inserted 23 amino acids in the RNA binding domain (RBD) of the coronvirus spike glycoprotein which cannot be detected in any other human coronavirus. Moreover, the 2019-nCoV is not closely related to other alpha, beta and gamma human Coronavirus, including MERS-CoV. The current study concluded that 2019-nCoV is more likely believed to originated from SARS-CoV. The probability is more vital to be originated from the strain isolated in China in 2020, which is coincident with the spraed of COVID-19 in the same country. The phyloepidemiologic analyses suggested that the coronaviruses are circulating in human hosts evolving gradually by times in response to the different environment stimuli facing the virus inside the host in different geographical areas. Furthermore, the analysis showed the flow of transmission, and evolutionary changes of SARS-CoV2 which may be directed from the transmission of SARS-CoV from human to Bat and Pangolin then jumped to human again in the crowded market Wuhan city in China.

Phylogenetic analysis of spike and envelope p roteins for a number of bat coronaviruses for understanding the hypothesis of possible origin for the novel 2019- nCoV

Coronavirus Disease 19 (COVID-19) emergence reveals globally a great health issue and due to the limited information and knowledge on the origin of this novel coronavirus 2019 (2019-nCoV). Therefore, this study aims to investigate the evolution and analysis of molecular epidemiology for both Spike and Envelope proteins of 20 available complete genome sequences of different bat coronaviruses including 2019-nCoV in order to find out which type of bat coronaviruses is more likely to be the origin of this new 2019-nCoV and also multiple amino acid sequences of Envelope protein for all bat coronaviruses were aligned for the purpose of finding the greater probability of novel 2019-nCoV original host among bat coronaviruses. Phylogenetic tree analysis for Spike protein revealed that all 2019-nCoV related coronaviruses isolated from these species of species are discovered in China and Hong Kong and the Middle East bat are less likely to contribute in spreading or to become the origin of 2019-nCoV and all coronaviruses that from Hong Kong and China are located into one clade next to the clade that contains 2019-nCoV coronaviruses which indicates that this group of coronaviruses are genetically different for 2019-nCoV;moreover, Hong Kong and USA bat coronaviruses does not contain the bat coronavirus from China and are located into one clade far from the clade that contains 2019-nCoV indicates that all coronaviruses are genetically very different from 2019-nCoV, and USA bat coronavirus may has no role in generating of 2019-nCoV. The phylogenetic trees analysis of Envelope protein showed that Envelope protein of different coronaviruses are more similar in comparison to Spike protein, USA bat coronavirus has a relatively closeness relationship to 2019-nCoV. Furthermore, Envelope protein alignment showed the closely related amino acid sequence which confirms that the outcomes of phylogenetic tree analysis in which that these bat coronaviruses have genetically close relationship together and more interestingly amino acid sequence (MG772934.1) shows 100% identity with the amino acid sequence of 2019-nCoV (NC 045512.2) and the same virus has a close relationship in both Spike and Envelope due to that in both phylogenetic tree analysis are neighbored with 2019-nCoV in the same clade.

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.