Development of one-step SYBR Green real-time RT-PCR for quantifying bovine viral diarrhea virus type-1 and its comparison with conventional RT-PCR

Background Bovine viral diarrhea virus (BVDV) is a worldwide pathogen in cattle and acts as a surrogate model for hepatitis C virus (HCV). One-step real-time fluorogenic quantitative reverse transcription polymerase chain reaction (RT-PCR) assay based on SYBR Green I dye has not been established for BVDV detection. This study aims to develop a quantitative one-step RT-PCR assay to detect BVDV type-1 in cell culture. Results One-step quantitative SYBR Green I RT-PCR was developed by amplifying cDNA template from viral RNA and using in vitro transcribed BVDV RNA to establish a standard curve. The assay had a detection limit as low as 100 copies/ml of BVDV RNA, a reaction efficiency of 103.2%, a correlation coefficient (R2) of 0.995, and a maximum intra-assay CV of 2.63%. It was 10-fold more sensitive than conventional RT-PCR and can quantitatively detect BVDV RNA levels from 10-fold serial dilutions of titrated viruses containing a titer from 10-1 to 10-5 TCID50, without non-specific amplification. Melting curve analysis showed no primer-dimers and non-specific products. Conclusions The one-step SYBR Green I RT-PCR is specific, sensitive and reproducible for the quantification of BVDV in cell culture. This one-step SYBR Green I RT-PCR strategy may be further optimized as a reliable assay for diagnosing and monitoring BVDV infection in animals. It may also be applied to evaluate candidate agents against HCV using BVDV cell culture model

Development of one-step SYBR Green real-time RT-PCR for quantifying bovine viral diarrhea virus type-1 and its comparison with conventional RT-PCR

<p>Abstract</p> <p>Background</p> <p>Bovine viral diarrhea virus (BVDV) is a worldwide pathogen in cattle and acts as a surrogate model for hepatitis C virus (HCV). One-step real-time fluorogenic quantitative reverse transcription polymerase chain reaction (RT-PCR) assay based on SYBR Green I dye has not been established for BVDV detection. This study aims to develop a quantitative one-step RT-PCR assay to detect BVDV type-1 in cell culture.</p> <p>Results</p> <p>One-step quantitative SYBR Green I RT-PCR was developed by amplifying cDNA template from viral RNA and using <it>in vitro </it>transcribed BVDV RNA to establish a standard curve. The assay had a detection limit as low as 100 copies/ml of BVDV RNA, a reaction efficiency of 103.2%, a correlation coefficient (R²) of 0.995, and a maximum intra-assay CV of 2.63%. It was 10-fold more sensitive than conventional RT-PCR and can quantitatively detect BVDV RNA levels from 10-fold serial dilutions of titrated viruses containing a titer from 10^-1to 10^-5TCID₅₀, without non-specific amplification. Melting curve analysis showed no primer-dimers and non-specific products.</p> <p>Conclusions</p> <p>The one-step SYBR Green I RT-PCR is specific, sensitive and reproducible for the quantification of BVDV in cell culture. This one-step SYBR Green I RT-PCR strategy may be further optimized as a reliable assay for diagnosing and monitoring BVDV infection in animals. It may also be applied to evaluate candidate agents against HCV using BVDV cell culture model.</p

Distinct mechanisms define murine B cell lineage immunoglobulin heavy chain (IgH) repertoires

Processes that define immunoglobulin repertoires are commonly presumed to be the same for all murine B cells. However, studies here that couple high-dimensional FACS sorting with large-scale quantitative IgH deep-sequencing demonstrate that B-1a IgH repertoire differs dramatically from the follicular and marginal zone B cells repertoires and is defined by distinct mechanisms. We track B-1a cells from their early appearance in neonatal spleen to their long-term residence in adult peritoneum and spleen. We show that de novo B-1a IgH rearrangement mainly occurs during the first few weeks of life, after which their repertoire continues to evolve profoundly, including convergent selection of certain V(D)J rearrangements encoding specific CDR3 peptides in all adults and progressive introduction of hypermutation and class-switching as animals age. This V(D)J selection and AID-mediated diversification operate comparably in germ-free and conventional mice, indicating these unique B-1a repertoire-defining mechanisms are driven by antigens that are not derived from microbiota

Metagenomic Next-Generation Sequencing in the Diagnosis of HHV-1 Reactivation in a Critically Ill COVID-19 Patient: A Case Report

Background: Secondary infections pose tremendous challenges in Coronavirus disease 2019 (COVID-19) treatment and are associated with higher mortality rates. Clinicians face of the challenge of diagnosing viral infections because of low sensitivity of available laboratory tests. Case Presentation: A 66-year-old woman initially manifested fever and shortness of breath. She was diagnosed as critically ill with COVID-19 using quantitative reverse transcription PCR (RT-qPCR) and treated with antiviral therapy, ventilator and extracorporeal membrane oxygenation (ECMO). However, after the condition was relatively stabled for a few days, the patient deteriorated with fever, frequent cough, increased airway secretions, and increased exudative lesions in the lower right lung on chest X-rays, showing the possibility of a newly acquired infection, though sputum bacterial and fungal cultures and smears showed negative results. Using metagenomic next-generation sequencing (mNGS), we identified a reactivation of latent human herpes virus type 1 (HHV-1) in the respiratory tract, blood and gastrointestinal tract, resulting in a worsened clinical course in a critically ill COVID-19 patient on ECMO. Anti-HHV-1 therapy guided by these sequencing results effectively decreased HHV-1 levels, and improved the patient\u27s clinical condition. After 49 days on ECMO and 67 days on the ventilator, the 66-year-old patient recovered and was discharged. Conclusions: This case report demonstrates the potential value of mNGS for evidence-based treatment, and suggests that potential reactivation of latent viruses should be considered in critically ill COVID-19 patients

Distinct mechanisms define murine B cell lineage immunoglobulin heavy chain (IgH) repertoires

Abstract Processes that define immunoglobulin repertoires are commonly presumed to be the same for all murine B cells. However, studies here that couple high-dimensional FACS sorting with large-scale quantitative IgH deep-sequencing demonstrate that B-1a IgH repertoire differs dramatically from the follicular and marginal zone B cells repertoires and is defined by distinct mechanisms. We track B-1a cells from their early appearance in neonatal spleen to their long-term residence in adult peritoneum and spleen. We show that de novo B-1a IgH rearrangement mainly occurs during the first few weeks of life, after which their repertoire continues to evolve profoundly, including convergent selection of certain V(D)J rearrangements encoding specific CDR3 peptides in all adults and progressive introduction of hypermutation and class-switching as animals age. This V(D)J selection and AID-mediated diversification operate comparably in germ-free and conventional mice, indicating these unique B-1a repertoire-defining mechanisms are driven by antigens that are not derived from microbiota

Identification of genes related to the development of bamboo rhizome bud

Bamboo (Phyllostachys praecox) is one of the largest members of the grass family Poaceae, and is one of the most economically important crops in Asia. However, complete knowledge of bamboo development and its molecular mechanisms is still lacking. In the present study, the differences in anatomical structure among rhizome buds, rhizome shoots, and bamboo shoots were compared, and several genes related to the development of the bamboo rhizome bud were identified. The rice cross-species microarray hybridization showed a total of 318 up-regulated and 339 down-regulated genes, including those involved in regulation and signalling, metabolism, and stress, and also cell wall-related genes, in the bamboo rhizome buds versus the leaves. By referring to the functional dissection of the homologous genes from Arabidopsis and rice, the putative functions of the 52 up-regulated genes in the bamboo rhizome bud were described. Six genes related to the development of the bamboo rhizome bud were further cloned and sequenced. These show 66–90% nucleotide identity and 68–98% amino acid identity with the homologous rice genes. The expression patterns of these genes revealed significant differences in rhizome shoots, rhizome buds, bamboo shoots, leaves, and young florets. Furthermore, in situ hybridization showed that the PpRLK1 gene is expressed in the procambium and is closely related to meristem development of bamboo shoots. The PpHB1 gene is expressed at the tips of bamboo shoots and procambium, and is closely related to rhizome bud formation and procambial development. To our knowledge, this is the first report that uses rice cross-species hybridization to identify genes related to bamboo rhizome bud development, and thereby contributes to the further understanding of the molecular mechanism involved in bamboo rhizome bud development

Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies

Social media platforms have been contributing to disaster management during the past several years. Text mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes, such as caution and advice, to better understand the meaning and leverage useful information from the social media text content. However, these methods are mostly event specific and difficult to generalize for cross-event classifications. In other words, traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future event. This research examines the capability of a convolutional neural network (CNN) model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy, Harvey, and Irma. The performance of the CNN model is compared to two traditional machine learning methods: support vector machine (SVM) and logistic regression (LR). Experiment results showed that CNN models achieved a consistently better accuracy for both single event and cross-event evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy results. This indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness