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LitCovid-PubTator

LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T2","span":{"begin":299,"end":304},"obj":"Body_part"},{"id":"T3","span":{"begin":309,"end":315},"obj":"Body_part"}],"attributes":[{"id":"A2","pred":"uberon_id","subj":"T2","obj":"http://purl.obolibrary.org/obo/UBERON_0000178"},{"id":"A3","pred":"uberon_id","subj":"T3","obj":"http://purl.obolibrary.org/obo/UBERON_0000341"}],"text":"Material \u0026 Methods\nThe clinical samples were referred by the hospital authorities through the Kerala State Health Services for diagnostic purposes. Further samples were received from different parts of India for establishing the presence of SARS-CoV-2.\nDetection of SARS-CoV-2 in suspected samples: Blood and throat swab (TS) specimens were collected from the suspected cases that complied with the case definition of SARS-CoV-2 infection as per the guidelines of the Ministry of Health and Family Welfare14. The TS was collected in viral transport medium. These samples were referred to the ICMR-NIV, Pune, India (which is the national reference laboratory for India, also referred as the government's apex laboratory). As of February 29, 2020, 881 samples of suspected cases referred from different States, with a travel history to Wuhan, China, and other SARS-CoV-2-affected countries, were screened.\nThe viral RNA was extracted from the TS sample using the Magmax RNA extraction kit (Applied Biosystems, USA) as per the manufacturer's instructions. The extracted RNA was immediately used for testing the presence of SARS-CoV-2 using the real-time RT-PCR protocol published by the WHO12 for the detection of RdRp (1), RdRp (2), E gene and N gene. RNase P gene was used as the internal control for the analysis. Confirmatory laboratory tests were performed as per the WHO-recommended test protocols13. These samples were also sequenced using the NGS approach to retrieve the complete genome of the virus.\nNGS of SARS-CoV-2 from India - Phylogenetic analysis and molecular characterization: The total RNA of three positive TS specimens from Kerala, was extracted from 250-300 μl of the SARS-CoV-2 real-time RT-PCR positive samples. QIAamp Viral RNA extraction kit (QIAGEN, Hilden, Germany) was used according to the manufacturer's instructions. The extracted RNA was further quantified using a Qubit RNA High-Sensitivity kit (Invitrogen, USA). RNA libraries were prepared as per the earlier-defined protocol and quantified using KAPA Library Quantification Kit (Kapa Biosystems, Roche Diagnostics Corporation, USA) as per the manufacturer's protocol. Further, individual libraries were neutralized and loaded on the Miniseq platform (Illumina, USA). The detailed protocols for the steps undertaken have been published earlier1516. The data generated from the machine were analyzed using CLC genomics workbench version 11.0 (CLC, QIAGEN, Germany). Reference-based mapping was performed to retrieve the sequence of the SARS-CoV-2.\nFull-length genome sequences of SARS-CoV-2 were downloaded from the GISAID database17 (Supplementary Table I (available from http://www.ijmr.org.in/articles/2020/151/2/images/IndianJMedRes_2020_151_2_200_281471_sm5.pdf)). Multiple sequence alignment was performed using the MEGA software version 7.018 with retrieved sequences from two of the three positive cases and the available GISAID sequences. A phylogenetic tree was generated using the neighbour joining method and the Kimura-2-parameter as the nucleotide (nt) substitution model with 1000 bootstrap replications as implemented in MEGA software18. Per cent nucleotide divergence and amino acid (aa) divergence were calculated using the p-distance method18. Mutations specific to the Indian SARS-CoV-2 viruses were identified by comparing the coding regions with respect to the SARS-CoV-2, Wuhan, China (Wuhan hu-1).\nThree-dimensional (3D) model of the spike protein and epitope prediction: The pre-fusion structure of the Indian case 1 SARS-CoV-2 spike (S) glycoprotein was modelled using the Swiss-Model server (https://swissmodel.expasy.org/interactive) and the corresponding S protein of Wuhan-Hu-1 (6VSB.PDB) as the template (99.97% identity). Sequential (linear) B-cell epitopes were predicted using BepiPred-2.0 server (http://www.cbs.dtu.dk/services/BepiPred/). The ABCpred prediction tool (http://crdd.osdd.net/raghava/abcpred/) was also used to identify the B-cell epitopes in the Indian SARS-CoV-2 sequence. The epitope prediction probability of \u003e0.8 was set to increase the specificity of the peptide stretch. The overlapping epitopes predicted by BepiPred-2.0 online server and the ABCpred prediction tool were identified. The antigenicity of the shortlisted peptide sequences was further predicted using the Vaxijen online server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) with a default threshold of 0.4.\nDiscontinuous epitopes on the modelled structure of the Indian case 1 SARS-CoV-2 spike protein were predicted using the online servers, Ellipro (http://tools.iedb.org/ellipro/) and DiscoTope 2.0 (http://tools.iedb.org/discotope/), integrated in the Immune Epitope Database. Ellipro predicts epitopes based on the protusion index (PI), wherein the protein shape is approximated as an ellipsoid (Ref for Ellipro and DiscoTope). An ellipsoid with the PI value of 0.8 indicates that 80 per cent of the residues are within the ellipsoid and 20 per cent are outside. All residues that are outside the 80 per cent ellipsoid will have a score of 0.8. Residues with larger scores are associated with greater solvent accessibility. The PI value was set to a score of 0.8. DiscoTope predicts epitopes using 3D structure and half-sphere exposure as a surface measure in a novel spatial neighbourhood definition method. Default values were set for sensitivity (0.47) and specificity (0.75) for selecting the amino acids forming discontinuous epitopes. A sensitivity of 0.47 means that 47 per cent of the epitope residues are predicted as part of the epitopes, while a specificity of 0.75 means that 25 per cent of the non-epitope residues are predicted as part of the epitopes. Outputs from both the methods were combined, and the final regions were mapped on the modelled 3D-structure as the most probable conformational epitopes. In addition, we also predicted N-linked glycosylation sites in the S protein using NetNGlyc 1.0 Server (http://www.cbs.dtu.dk/services/NetNGlyc/). The spike proteins were also screened for the presence of potential epitopes presented by major histocompatibility complex (MHC) class I molecules to cytotoxic T lymphocytes (CTLs). The online NetCTL1.2 server (http://www.cbs.dtu.dk/services/NetCTL/) based on machine learning techniques such as artificial neural network (ANN) and support vector machine (SVM) was used to predict the T-cell epitopes. The prediction was made for all the human leucocyte antigen (HLA) supertypes and the available human alleles. The C terminal cleavage, weight of transport-associated protein (TAP) efficiency and threshold for identification were kept as default. VaxiJen v2.0 tool was used to predict the antigenicity of the predicted epitopes (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html). The sequences were further screened to be potential epitopes using the CTLPred online server (http://crdd.osdd.net/raghava/ctlpred/).\nThe ability of the predicted linear B-cell and the T-cell epitopes to mount interferon-gamma (IFN-γ) response was assessed using the IFNepitope (http://crdd.osdd.net/raghava/ifnepitope/index.php)."}

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T7","span":{"begin":533,"end":548},"obj":"http://purl.obolibrary.org/obo/GO_0046794"},{"id":"T8","span":{"begin":539,"end":548},"obj":"http://purl.obolibrary.org/obo/GO_0006810"},{"id":"T9","span":{"begin":1250,"end":1257},"obj":"http://purl.obolibrary.org/obo/GO_0004526"},{"id":"T10","span":{"begin":5873,"end":5895},"obj":"http://purl.obolibrary.org/obo/GO_0006487"},{"id":"T11","span":{"begin":5882,"end":5895},"obj":"http://purl.obolibrary.org/obo/GO_0070085"},{"id":"T12","span":{"begin":6079,"end":6111},"obj":"http://purl.obolibrary.org/obo/GO_0046776"},{"id":"T13","span":{"begin":6113,"end":6116},"obj":"http://purl.obolibrary.org/obo/GO_0046776"},{"id":"T14","span":{"begin":6257,"end":6265},"obj":"http://purl.obolibrary.org/obo/GO_0007612"},{"id":"T15","span":{"begin":6536,"end":6545},"obj":"http://purl.obolibrary.org/obo/GO_0006810"}],"text":"Material \u0026 Methods\nThe clinical samples were referred by the hospital authorities through the Kerala State Health Services for diagnostic purposes. Further samples were received from different parts of India for establishing the presence of SARS-CoV-2.\nDetection of SARS-CoV-2 in suspected samples: Blood and throat swab (TS) specimens were collected from the suspected cases that complied with the case definition of SARS-CoV-2 infection as per the guidelines of the Ministry of Health and Family Welfare14. The TS was collected in viral transport medium. These samples were referred to the ICMR-NIV, Pune, India (which is the national reference laboratory for India, also referred as the government's apex laboratory). As of February 29, 2020, 881 samples of suspected cases referred from different States, with a travel history to Wuhan, China, and other SARS-CoV-2-affected countries, were screened.\nThe viral RNA was extracted from the TS sample using the Magmax RNA extraction kit (Applied Biosystems, USA) as per the manufacturer's instructions. The extracted RNA was immediately used for testing the presence of SARS-CoV-2 using the real-time RT-PCR protocol published by the WHO12 for the detection of RdRp (1), RdRp (2), E gene and N gene. RNase P gene was used as the internal control for the analysis. Confirmatory laboratory tests were performed as per the WHO-recommended test protocols13. These samples were also sequenced using the NGS approach to retrieve the complete genome of the virus.\nNGS of SARS-CoV-2 from India - Phylogenetic analysis and molecular characterization: The total RNA of three positive TS specimens from Kerala, was extracted from 250-300 μl of the SARS-CoV-2 real-time RT-PCR positive samples. QIAamp Viral RNA extraction kit (QIAGEN, Hilden, Germany) was used according to the manufacturer's instructions. The extracted RNA was further quantified using a Qubit RNA High-Sensitivity kit (Invitrogen, USA). RNA libraries were prepared as per the earlier-defined protocol and quantified using KAPA Library Quantification Kit (Kapa Biosystems, Roche Diagnostics Corporation, USA) as per the manufacturer's protocol. Further, individual libraries were neutralized and loaded on the Miniseq platform (Illumina, USA). The detailed protocols for the steps undertaken have been published earlier1516. The data generated from the machine were analyzed using CLC genomics workbench version 11.0 (CLC, QIAGEN, Germany). Reference-based mapping was performed to retrieve the sequence of the SARS-CoV-2.\nFull-length genome sequences of SARS-CoV-2 were downloaded from the GISAID database17 (Supplementary Table I (available from http://www.ijmr.org.in/articles/2020/151/2/images/IndianJMedRes_2020_151_2_200_281471_sm5.pdf)). Multiple sequence alignment was performed using the MEGA software version 7.018 with retrieved sequences from two of the three positive cases and the available GISAID sequences. A phylogenetic tree was generated using the neighbour joining method and the Kimura-2-parameter as the nucleotide (nt) substitution model with 1000 bootstrap replications as implemented in MEGA software18. Per cent nucleotide divergence and amino acid (aa) divergence were calculated using the p-distance method18. Mutations specific to the Indian SARS-CoV-2 viruses were identified by comparing the coding regions with respect to the SARS-CoV-2, Wuhan, China (Wuhan hu-1).\nThree-dimensional (3D) model of the spike protein and epitope prediction: The pre-fusion structure of the Indian case 1 SARS-CoV-2 spike (S) glycoprotein was modelled using the Swiss-Model server (https://swissmodel.expasy.org/interactive) and the corresponding S protein of Wuhan-Hu-1 (6VSB.PDB) as the template (99.97% identity). Sequential (linear) B-cell epitopes were predicted using BepiPred-2.0 server (http://www.cbs.dtu.dk/services/BepiPred/). The ABCpred prediction tool (http://crdd.osdd.net/raghava/abcpred/) was also used to identify the B-cell epitopes in the Indian SARS-CoV-2 sequence. The epitope prediction probability of \u003e0.8 was set to increase the specificity of the peptide stretch. The overlapping epitopes predicted by BepiPred-2.0 online server and the ABCpred prediction tool were identified. The antigenicity of the shortlisted peptide sequences was further predicted using the Vaxijen online server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) with a default threshold of 0.4.\nDiscontinuous epitopes on the modelled structure of the Indian case 1 SARS-CoV-2 spike protein were predicted using the online servers, Ellipro (http://tools.iedb.org/ellipro/) and DiscoTope 2.0 (http://tools.iedb.org/discotope/), integrated in the Immune Epitope Database. Ellipro predicts epitopes based on the protusion index (PI), wherein the protein shape is approximated as an ellipsoid (Ref for Ellipro and DiscoTope). An ellipsoid with the PI value of 0.8 indicates that 80 per cent of the residues are within the ellipsoid and 20 per cent are outside. All residues that are outside the 80 per cent ellipsoid will have a score of 0.8. Residues with larger scores are associated with greater solvent accessibility. The PI value was set to a score of 0.8. DiscoTope predicts epitopes using 3D structure and half-sphere exposure as a surface measure in a novel spatial neighbourhood definition method. Default values were set for sensitivity (0.47) and specificity (0.75) for selecting the amino acids forming discontinuous epitopes. A sensitivity of 0.47 means that 47 per cent of the epitope residues are predicted as part of the epitopes, while a specificity of 0.75 means that 25 per cent of the non-epitope residues are predicted as part of the epitopes. Outputs from both the methods were combined, and the final regions were mapped on the modelled 3D-structure as the most probable conformational epitopes. In addition, we also predicted N-linked glycosylation sites in the S protein using NetNGlyc 1.0 Server (http://www.cbs.dtu.dk/services/NetNGlyc/). The spike proteins were also screened for the presence of potential epitopes presented by major histocompatibility complex (MHC) class I molecules to cytotoxic T lymphocytes (CTLs). The online NetCTL1.2 server (http://www.cbs.dtu.dk/services/NetCTL/) based on machine learning techniques such as artificial neural network (ANN) and support vector machine (SVM) was used to predict the T-cell epitopes. The prediction was made for all the human leucocyte antigen (HLA) supertypes and the available human alleles. The C terminal cleavage, weight of transport-associated protein (TAP) efficiency and threshold for identification were kept as default. VaxiJen v2.0 tool was used to predict the antigenicity of the predicted epitopes (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html). The sequences were further screened to be potential epitopes using the CTLPred online server (http://crdd.osdd.net/raghava/ctlpred/).\nThe ability of the predicted linear B-cell and the T-cell epitopes to mount interferon-gamma (IFN-γ) response was assessed using the IFNepitope (http://crdd.osdd.net/raghava/ifnepitope/index.php)."}

LitCovid-sentences

2_test

{"project":"2_test","denotations":[{"id":"32242873-29664366-47036564","span":{"begin":2326,"end":2328},"obj":"29664366"},{"id":"32242873-31565258-47036565","span":{"begin":2613,"end":2615},"obj":"31565258"},{"id":"32242873-27004904-47036566","span":{"begin":2828,"end":2831},"obj":"27004904"},{"id":"32242873-27004904-47036567","span":{"begin":3132,"end":3134},"obj":"27004904"},{"id":"32242873-27004904-47036568","span":{"begin":3241,"end":3243},"obj":"27004904"},{"id":"T53539","span":{"begin":2326,"end":2328},"obj":"29664366"},{"id":"T62421","span":{"begin":2613,"end":2615},"obj":"31565258"},{"id":"T79581","span":{"begin":2828,"end":2831},"obj":"27004904"},{"id":"T13164","span":{"begin":3132,"end":3134},"obj":"27004904"},{"id":"T93390","span":{"begin":3241,"end":3243},"obj":"27004904"}],"text":"Material \u0026 Methods\nThe clinical samples were referred by the hospital authorities through the Kerala State Health Services for diagnostic purposes. Further samples were received from different parts of India for establishing the presence of SARS-CoV-2.\nDetection of SARS-CoV-2 in suspected samples: Blood and throat swab (TS) specimens were collected from the suspected cases that complied with the case definition of SARS-CoV-2 infection as per the guidelines of the Ministry of Health and Family Welfare14. The TS was collected in viral transport medium. These samples were referred to the ICMR-NIV, Pune, India (which is the national reference laboratory for India, also referred as the government's apex laboratory). As of February 29, 2020, 881 samples of suspected cases referred from different States, with a travel history to Wuhan, China, and other SARS-CoV-2-affected countries, were screened.\nThe viral RNA was extracted from the TS sample using the Magmax RNA extraction kit (Applied Biosystems, USA) as per the manufacturer's instructions. The extracted RNA was immediately used for testing the presence of SARS-CoV-2 using the real-time RT-PCR protocol published by the WHO12 for the detection of RdRp (1), RdRp (2), E gene and N gene. RNase P gene was used as the internal control for the analysis. Confirmatory laboratory tests were performed as per the WHO-recommended test protocols13. These samples were also sequenced using the NGS approach to retrieve the complete genome of the virus.\nNGS of SARS-CoV-2 from India - Phylogenetic analysis and molecular characterization: The total RNA of three positive TS specimens from Kerala, was extracted from 250-300 μl of the SARS-CoV-2 real-time RT-PCR positive samples. QIAamp Viral RNA extraction kit (QIAGEN, Hilden, Germany) was used according to the manufacturer's instructions. The extracted RNA was further quantified using a Qubit RNA High-Sensitivity kit (Invitrogen, USA). RNA libraries were prepared as per the earlier-defined protocol and quantified using KAPA Library Quantification Kit (Kapa Biosystems, Roche Diagnostics Corporation, USA) as per the manufacturer's protocol. Further, individual libraries were neutralized and loaded on the Miniseq platform (Illumina, USA). The detailed protocols for the steps undertaken have been published earlier1516. The data generated from the machine were analyzed using CLC genomics workbench version 11.0 (CLC, QIAGEN, Germany). Reference-based mapping was performed to retrieve the sequence of the SARS-CoV-2.\nFull-length genome sequences of SARS-CoV-2 were downloaded from the GISAID database17 (Supplementary Table I (available from http://www.ijmr.org.in/articles/2020/151/2/images/IndianJMedRes_2020_151_2_200_281471_sm5.pdf)). Multiple sequence alignment was performed using the MEGA software version 7.018 with retrieved sequences from two of the three positive cases and the available GISAID sequences. A phylogenetic tree was generated using the neighbour joining method and the Kimura-2-parameter as the nucleotide (nt) substitution model with 1000 bootstrap replications as implemented in MEGA software18. Per cent nucleotide divergence and amino acid (aa) divergence were calculated using the p-distance method18. Mutations specific to the Indian SARS-CoV-2 viruses were identified by comparing the coding regions with respect to the SARS-CoV-2, Wuhan, China (Wuhan hu-1).\nThree-dimensional (3D) model of the spike protein and epitope prediction: The pre-fusion structure of the Indian case 1 SARS-CoV-2 spike (S) glycoprotein was modelled using the Swiss-Model server (https://swissmodel.expasy.org/interactive) and the corresponding S protein of Wuhan-Hu-1 (6VSB.PDB) as the template (99.97% identity). Sequential (linear) B-cell epitopes were predicted using BepiPred-2.0 server (http://www.cbs.dtu.dk/services/BepiPred/). The ABCpred prediction tool (http://crdd.osdd.net/raghava/abcpred/) was also used to identify the B-cell epitopes in the Indian SARS-CoV-2 sequence. The epitope prediction probability of \u003e0.8 was set to increase the specificity of the peptide stretch. The overlapping epitopes predicted by BepiPred-2.0 online server and the ABCpred prediction tool were identified. The antigenicity of the shortlisted peptide sequences was further predicted using the Vaxijen online server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) with a default threshold of 0.4.\nDiscontinuous epitopes on the modelled structure of the Indian case 1 SARS-CoV-2 spike protein were predicted using the online servers, Ellipro (http://tools.iedb.org/ellipro/) and DiscoTope 2.0 (http://tools.iedb.org/discotope/), integrated in the Immune Epitope Database. Ellipro predicts epitopes based on the protusion index (PI), wherein the protein shape is approximated as an ellipsoid (Ref for Ellipro and DiscoTope). An ellipsoid with the PI value of 0.8 indicates that 80 per cent of the residues are within the ellipsoid and 20 per cent are outside. All residues that are outside the 80 per cent ellipsoid will have a score of 0.8. Residues with larger scores are associated with greater solvent accessibility. The PI value was set to a score of 0.8. DiscoTope predicts epitopes using 3D structure and half-sphere exposure as a surface measure in a novel spatial neighbourhood definition method. Default values were set for sensitivity (0.47) and specificity (0.75) for selecting the amino acids forming discontinuous epitopes. A sensitivity of 0.47 means that 47 per cent of the epitope residues are predicted as part of the epitopes, while a specificity of 0.75 means that 25 per cent of the non-epitope residues are predicted as part of the epitopes. Outputs from both the methods were combined, and the final regions were mapped on the modelled 3D-structure as the most probable conformational epitopes. In addition, we also predicted N-linked glycosylation sites in the S protein using NetNGlyc 1.0 Server (http://www.cbs.dtu.dk/services/NetNGlyc/). The spike proteins were also screened for the presence of potential epitopes presented by major histocompatibility complex (MHC) class I molecules to cytotoxic T lymphocytes (CTLs). The online NetCTL1.2 server (http://www.cbs.dtu.dk/services/NetCTL/) based on machine learning techniques such as artificial neural network (ANN) and support vector machine (SVM) was used to predict the T-cell epitopes. The prediction was made for all the human leucocyte antigen (HLA) supertypes and the available human alleles. The C terminal cleavage, weight of transport-associated protein (TAP) efficiency and threshold for identification were kept as default. VaxiJen v2.0 tool was used to predict the antigenicity of the predicted epitopes (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html). The sequences were further screened to be potential epitopes using the CTLPred online server (http://crdd.osdd.net/raghava/ctlpred/).\nThe ability of the predicted linear B-cell and the T-cell epitopes to mount interferon-gamma (IFN-γ) response was assessed using the IFNepitope (http://crdd.osdd.net/raghava/ifnepitope/index.php)."}

PMC:7258756 / 6237-13345 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7258756 / 6237-13345 JSON TXT