scholarly journals The Intragenesis and Synthetic Biology Approach towards Accelerating Genetic Gains on Strawberry: Development of New Tools to Improve Fruit Quality and Resistance to Pathogens

Plants ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 57
Author(s):  
Victoria Súnico ◽  
José Javier Higuera ◽  
Francisco J. Molina-Hidalgo ◽  
Rosario Blanco-Portales ◽  
Enriqueta Moyano ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Fruit Quality ◽  
Dna Sequences ◽  
Rapid Development ◽  
Crop Protection ◽  
Pathogen Resistance ◽  
Gus Expression ◽  
Sequencing Data ◽  
Genetic Gains ◽  
Endogenous Genes

Under climate change, the spread of pests and pathogens into new environments has a dramatic effect on crop protection control. Strawberry (Fragaria spp.) is one the most profitable crops of the Rosaceae family worldwide, but more than 50 different genera of pathogens affect this species. Therefore, accelerating the improvement of fruit quality and pathogen resistance in strawberry represents an important objective for breeding and reducing the usage of pesticides. New genome sequencing data and bioinformatics tools has provided important resources to expand the use of synthetic biology-assisted intragenesis strategies as a powerful tool to accelerate genetic gains in strawberry. In this paper, we took advantage of these innovative approaches to create four RNAi intragenic silencing cassettes by combining specific strawberry new promoters and pathogen defense-related candidate DNA sequences to increase strawberry fruit quality and resistance by silencing their corresponding endogenous genes, mainly during fruit ripening stages, thus avoiding any unwanted effect on plant growth and development. Using a fruit transient assay, GUS expression was detected by the two synthetic FvAAT2 and FvDOF2 promoters, both by histochemical assay and qPCR analysis of GUS transcript levels, thus ensuring the ability of the same to drive the expression of the silencing cassettes in this strawberry tissue. The approaches described here represent valuable new tools for the rapid development of improved strawberry lines.

Current RNA-based Therapeutics in Clinical Trials

2019 ◽  
Vol 19 (3) ◽  
pp. 172-196 ◽  
Author(s):  
Ling-Yan Zhou ◽  
Zhou Qin ◽  
Yang-Hui Zhu ◽  
Zhi-Yao He ◽  
Ting Xu
Keyword(s):  
Clinical Trials ◽  
Rapid Development ◽  
Genetic Diseases ◽  
Three Dimensional ◽  
Therapeutic Effects ◽  
Messenger Rnas ◽  
Small Interfering Rnas ◽  
Rna Modifications ◽  
Guide Rna ◽  
Endogenous Genes

Long-term research on various types of RNAs has led to further understanding of diverse mechanisms, which eventually resulted in the rapid development of RNA-based therapeutics as powerful tools in clinical disease treatment. Some of the developing RNA drugs obey the antisense mechanisms including antisense oligonucleotides, small interfering RNAs, microRNAs, small activating RNAs, and ribozymes. These types of RNAs could be utilized to inhibit/activate gene expression or change splicing to provide functional proteins. In the meantime, some others based on different mechanisms like modified messenger RNAs could replace the dysfunctional endogenous genes to manage some genetic diseases, and aptamers with special three-dimensional structures could bind to specific targets in a high-affinity manner. In addition, the recent most popular CRISPR-Cas technology, consisting of a crucial single guide RNA, could edit DNA directly to generate therapeutic effects. The desired results from recent clinical trials indicated the great potential of RNA-based drugs in the treatment of various diseases, but further studies on improving delivery materials and RNA modifications are required for the novel RNA-based drugs to translate to the clinic. This review focused on the advances and clinical studies of current RNA-based therapeutics, analyzed their challenges and prospects.

scholarly journals A Whole Virion Vaccine for COVID-19 Produced via a Novel Inactivation Method and Preliminary Demonstration of Efficacy in an Animal Challenge Model

Vaccines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 340
Author(s):  
Izabela K Ragan ◽  
Lindsay M Hartson ◽  
Taru S Dutt ◽  
Andres Obregon-Henao ◽  
Rachel M Maison ◽  
...  
Keyword(s):  
Vaccine Candidate ◽  
Rapid Development ◽  
Neutralizing Antibody ◽  
Emerging Diseases ◽  
Effective Vaccine ◽  
Preliminary Evaluation ◽  
Sequencing Data ◽  
Post Challenge ◽  
Vaccine Candidates ◽  
The Impact

The COVID-19 pandemic has generated intense interest in the rapid development and evaluation of vaccine candidates for this disease and other emerging diseases. Several novel methods for preparing vaccine candidates are currently undergoing clinical evaluation in response to the urgent need to prevent the spread of COVID-19. In many cases, these methods rely on new approaches for vaccine production and immune stimulation. We report on the use of a novel method (SolaVAX) for production of an inactivated vaccine candidate and the testing of that candidate in a hamster animal model for its ability to prevent infection upon challenge with SARS-CoV-2 virus. The studies employed in this work included an evaluation of the levels of neutralizing antibody produced post-vaccination, levels of specific antibody sub-types to RBD and spike protein that were generated, evaluation of viral shedding post-challenge, flow cytometric and single cell sequencing data on cellular fractions and histopathological evaluation of tissues post-challenge. The results from this preliminary evaluation provide insight into the immunological responses occurring as a result of vaccination with the proposed vaccine candidate and the impact that adjuvant formulations, specifically developed to promote Th1 type immune responses, have on vaccine efficacy and protection against infection following challenge with live SARS-CoV-2. This data may have utility in the development of effective vaccine candidates broadly. Furthermore, the results of this preliminary evaluation suggest that preparation of a whole virion vaccine for COVID-19 using this specific photochemical method may have potential utility in the preparation of one such vaccine candidate.

scholarly journals A Synthetic Biology Approach Using Engineered Bacteria to Detect Perfluoroalkyl Substance (PFAS) Contamination in Water

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 801-807
Author(s):  
Nathaniel A Young ◽  
Ryan L Lambert ◽  
Angela M Buch ◽  
Christen L Dahl ◽  
Jackson D Harris ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Dna Sequences ◽  
Detection System ◽  
The United States ◽  
Detection Methods ◽  
Health Concern ◽  
Contaminated Water ◽  
Engineered Bacteria ◽  
Synthetic Biology Approach ◽  
Rhodococcus Jostii

ABSTRACT Introduction Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic compounds used industrially for a wide variety of applications. These PFAS compounds are very stable and persist in the environment. The PFAS contamination is a growing health issue as these compounds have been reported to impact human health and have been detected in both domestic and global water sources. Contaminated water found on military bases poses a potentially serious health concern for active duty military, their families, and the surrounding communities. Previous detection methods for PFAS in contaminated water samples require expensive and time-consuming testing protocols that limit the ability to detect this important global pollutant. The main objective of this work was to develop a novel detection system that utilizes a biological reporter and engineered bacteria as a way to rapidly and efficiently detect PFAS contamination. Materials and Methods The United States Air Force Academy International Genetically Engineered Machine team is genetically engineering Rhodococcus jostii strain RHA1 to contain novel DNA sequences composed of a propane 2-monooxygenase alpha (prmA) promoter and monomeric red fluorescent protein (mRFP). The prmA promoter is activated in the presence of PFAS and transcribes the mRFP reporter. Results The recombinant R. jostii containing the prmA promoter and mRFP reporter respond to exposure of PFAS by activating gene expression of the mRFP. At 100 µM of perfluorooctanoic acid, the mRFP expression was increased 3-fold (qRT-PCR). Rhodococcus jostii without exposure to PFAS compounds had no mRFP expression. Conclusions This novel detection system represents a synthetic biology approach to more efficiently detect PFAS in contaminated samples. With further refinement and modifications, a similar system could be readily deployed in the field around the world to detect this critical pollutant.

scholarly journals Data Structures to Represent a Set of k -long DNA Sequences

2021 ◽  
Vol 54 (1) ◽  
pp. 1-22
Author(s):  
Rayan Chikhi ◽  
Jan Holub ◽  
Paul Medvedev
Keyword(s):  
Data Structures ◽  
Dna Sequences ◽  
Sequencing Data ◽  
String Algorithms ◽  
Fixed Length ◽  
The Past

The analysis of biological sequencing data has been one of the biggest applications of string algorithms. The approaches used in many such applications are based on the analysis of k -mers, which are short fixed-length strings present in a dataset. While these approaches are rather diverse, storing and querying a k -mer set has emerged as a shared underlying component. A set of k -mers has unique features and applications that, over the past 10 years, have resulted in many specialized approaches for its representation. In this survey, we give a unified presentation and comparison of the data structures that have been proposed to store and query a k -mer set. We hope this survey will serve as a resource for researchers in the field as well as make the area more accessible to researchers outside the field.

scholarly journals Towards end-to-end disease prediction from raw metagenomic data

2020 ◽  
Author(s):  
Maxence Queyrel ◽  
Edi Prifti ◽  
Jean-Daniel Zucker
Keyword(s):  
Dna Sequences ◽  
Real Life ◽  
Multiple Instance Learning ◽  
Disease Classification ◽  
Metagenomic Data ◽  
Numerical Representation ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
End To End ◽  
Bioinformatics Workflows

AbstractAnalysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and are stored as fastq files. Conventional processing pipelines consist multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Recent studies have demonstrated that training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimentionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life datasets as well a simulated one, we demonstrated that this original approach reached very high performances, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

scholarly journals Genomic and Molecular Perspectives of Host-pathogen Interaction and Resistance Strategies against White Rust in Oilseed Mustard

2020 ◽  
Vol 21 (3) ◽  
pp. 179-193
Author(s):  
Chatterjee Anupriya ◽  
Nirwan Shradha ◽  
Bandyopadhyay Prasun ◽  
Agnihotri Abha ◽  
Sharma Pankaj ◽  
...  
Keyword(s):  
Crop Protection ◽  
Resistance Mechanism ◽  
Pathogen Resistance ◽  
Susceptibility Genes ◽  
Rust Disease ◽  
Disease Response ◽  
White Rust ◽  
Oilseed Brassicas ◽  
Insight Into

: Oilseed brassicas stand as the second most valuable source of vegetable oil and the third most traded one across the globe. However, the yield can be severely affected by infections caused by phytopathogens. White rust is a major oomycete disease of oilseed brassicas resulting in up to 60% yield loss globally. So far, success in the development of oomycete resistant Brassicas through conventional breeding has been limited. Hence, there is an imperative need to blend conventional and frontier biotechnological means to breed for improved crop protection and yield. : This review provides a deep insight into the white rust disease and explains the oomycete-plant molecular events with special reference to Albugo candida describing the role of effector molecules, A. candida secretome, and disease response mechanism along with nucleotide-binding leucine-rich repeat receptor (NLR) signaling. Based on these facts, we further discussed the recent progress and future scopes of genomic approaches to transfer white rust resistance in the susceptible varieties of oilseed brassicas, while elucidating the role of resistance and susceptibility genes. Novel genomic technologies have been widely used in crop sustainability by deploying resistance in the host. Enrichment of NLR repertoire, over-expression of R genes, silencing of avirulent and disease susceptibility genes through RNA interference and CRSPR-Cas are technologies which have been successfully applied against pathogen-resistance mechanism. The article provides new insight into Albugo and Brassica genomics which could be useful for producing high yielding and WR resistant oilseed cultivars across the globe.

scholarly journals HTSeq - A Python framework to work with high-throughput sequencing data

2014 ◽  
Author(s):  
Simon Anders ◽  
Paul Theodor Pyl ◽  
Wolfgang Huber
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Rapid Development ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Standard Work ◽  
Data Formats ◽  
High Throughput Sequencing Data ◽  
Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

scholarly journals A set of experimentally validated, mutually orthogonal primers for combinatorially specifying genetic components

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Subu K Subramanian ◽  
William P Russ ◽  
Rama Ranganathan
Keyword(s):  
Synthetic Biology ◽  
High Throughput ◽  
Dna Sequences ◽  
Gene Synthesis ◽  
Modular Architecture ◽  
Design And Synthesis ◽  
Genetic Components ◽  
Polymerase Chain ◽  
Polymerase Chain Reaction Primers ◽  
Microarray Chips

Abstract The design and synthesis of novel genes and deoxyribonucleic acid (DNA) sequences is a central technique in synthetic biology. Current methods of high throughput gene synthesis use pooled oligonucleotides obtained from custom-designed DNA microarray chips, and rely on orthogonal (non-interacting) polymerase chain reaction primers to specifically de-multiplex, by amplification, the precise subset of oligonucleotides necessary to assemble a full length gene. The availability of a large validated set of mutually orthogonal primers is therefore a crucial reagent for high-throughput gene synthesis. Here, we present a set of 166 20-nucleotide primers that are experimentally verified to be non-interacting, capable of specifying 13 695 unique genes. These primers represent a valuable resource to the synthetic biology community for specifying genetic components that can be assembled through a scalable and modular architecture.

Application of high-throughput serological tests for controlling zoonotic viral infection in Amazon rainforest and as a strategy to prevent future pandemics (Preprint)

2021 ◽  
Author(s):  
Marcela Santiago Pacheco De Azevedo ◽  
Daniela Gomes Ribeiro ◽  
Felipe Rocha Da Silva Santos ◽  
Siomar De Castro Soares ◽  
Vasco Ariston De Carvalho Azevedo ◽  
...  
Keyword(s):  
Developing Countries ◽  
High Throughput ◽  
Low Income ◽  
Dna Sequences ◽  
Scientific Information ◽  
Preventive Health Care ◽  
Amazon Rainforest ◽  
Low Income Countries ◽  
Sequencing Data ◽  
Serological Tests

UNSTRUCTURED From the bubonic plague on the 14th century to the new Coronavirus disease 2019 (COVID-19), pandemics have profoundly changed societies function. Infectious disease outbreaks are getting shorter and shorter due to our densely populated cities, global travel, and nature mass exploration. In this regard, there is a particular concern about fires occurring in Brazil's Amazon rainforest, one of the most biodiverse places on earth that facilitates cross-species transmission giving rise to the emergency of new virulent pathogens. Situation is further complicated because Amazon spans across eight developing countries with limited preventive health care services. In this perspective, this review highlights the role of new methodologies best suited for epidemiological monitoring in low-income countries, such as high-throughput serological tests. Phage immunoprecipitation sequencing (Phip-Seq), for example, can evaluate antibody-repertoire binding specificities using oligonucleotides libraries encoding epitopes covering the DNA sequences from all human pathogenic viruses or all Arboviruses already described. After incubation with an individual's serum, these libraries can be immunoprecipitated for subsequent analysis by DNA sequencing. Data are analyzed revealing peptides recognized by the antibodies present in the sample. Being a technique at a relatively low cost, its implementation in developing countries is feasible and can generate very interesting scientific information.

scholarly journals Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Andrew Currin ◽  
Neil Swainston ◽  
Mark S Dunstan ◽  
Adrian J Jervis ◽  
Paul Mulherin ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Dna Sequencing ◽  
Cost Effective ◽  
Polymorphism Analysis ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Synthetic Dna ◽  
Design Build ◽  
Hardware Costs

Abstract Synthetic biology utilizes the Design–Build–Test–Learn pipeline for the engineering of biological systems. Typically, this requires the construction of specifically designed, large and complex DNA assemblies. The availability of cheap DNA synthesis and automation enables high-throughput assembly approaches, which generates a heavy demand for DNA sequencing to verify correctly assembled constructs. Next-generation sequencing is ideally positioned to perform this task, however with expensive hardware costs and bespoke data analysis requirements few laboratories utilize this technology in-house. Here a workflow for highly multiplexed sequencing is presented, capable of fast and accurate sequence verification of DNA assemblies using nanopore technology. A novel sample barcoding system using polymerase chain reaction is introduced, and sequencing data are analyzed through a bespoke analysis algorithm. Crucially, this algorithm overcomes the problem of high-error rate nanopore data (which typically prevents identification of single nucleotide variants) through statistical analysis of strand bias, permitting accurate sequence analysis with single-base resolution. As an example, 576 constructs (6 × 96 well plates) were processed in a single workflow in 72 h (from Escherichia coli colonies to analyzed data). Given our procedure’s low hardware costs and highly multiplexed capability, this provides cost-effective access to powerful DNA sequencing for any laboratory, with applications beyond synthetic biology including directed evolution, single nucleotide polymorphism analysis and gene synthesis.

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