Efficient production of saffron crocins and picrocrocin in Nicotiana benthamiana using a virus-driven system

Crocins and picrocrocin are glycosylated apocarotenoids responsible, respectively, for the color and the unique taste of the saffron spice, known as red gold due to its high price. Several studies have also shown the health-promoting properties of these compounds. However, their high costs hamper the wide use of these metabolites in the pharmaceutical sector. We have developed a virus-driven system to produce remarkable amounts of crocins and picrocrocin in adult Nicotiana benthamiana plants in only two weeks. The system consists of viral clones derived from tobacco etch potyvirus that express specific carotenoid cleavage dioxygenase (CCD) enzymes from Crocus sativus and Buddleja davidii. Metabolic analyses of infected tissues demonstrated that the sole virus-driven expression of C. sativus CsCCD2L or B. davidii BdCCD4.1 resulted in the production of crocins, picrocrocin and safranal. Using the recombinant virus that expressed CsCCD2L, accumulations of 0.2% of crocins and 0.8% of picrocrocin in leaf dry weight were reached in only two weeks. In an attempt to improve apocarotenoid content in N. benthamiana, co-expression of CsCCD2L with other carotenogenic enzymes, such as Pantoea ananatis phytoene synthase (PaCrtB) and saffron β-carotene hydroxylase 2 (BCH2), was performed using the same viral system. This combinatorial approach led to an additional crocin increase up to 0.35% in leaves in which CsCCD2L and PaCrtB were co-expressed. Considering that saffron apocarotenoids are costly harvested from flower stigma once a year, and that Buddleja spp. flowers accumulate lower amounts, this system may be an attractive alternative for the sustainable production of these appreciated metabolites.     

Projected climate and land use change alter western blacklegged tick phenology,seasonal host‐seeking suitability and human encounter risk in California

Global environmental change is having profound effects on the ecology of infectious disease systems, which are widely anticipated to become more pronounced under future climate and land use change. Arthropod vectors of disease are particularly sensitive to changes in abiotic conditions such as temperature and moisture availability. Recent research has focused on shifting environmental suitability for, and geographic distribution of, vector species under projected climate change scenarios. However, shifts in seasonal activity patterns, or phenology, may also have dramatic consequences for human exposure risk, local vector abundance and pathogen transmission dynamics. Moreover, changes in land use are likely to alter human–vector contact rates in ways that models of changing climate suitability are unlikely to capture. Here we used climate and land use projections for California coupled with seasonal species distribution models to explore the response of the western blacklegged tick (Ixodes pacificus), the primary Lyme disease vector in western North America, to projected climate and land use change. Specifically, we investigated how environmental suitability for tick host‐seeking changes seasonally, how the magnitude and direction of changing seasonal suitability differs regionally across California, and how land use change shifts human tick‐encounter risk across the state. We found vector responses to changing climate and land use vary regionally within California under different future scenarios. Under a hotter, drier scenario and more extreme land use change, the duration and extent of seasonal host‐seeking activity increases in northern California, but declines in the south. In contrast, under a hotter, wetter scenario seasonal host‐seeking declines in northern California, but increases in the south. Notably, regardless of future scenario, projected increases in developed land adjacent to current human population centers substantially increase potential human–vector encounter risk across the state. These results highlight regional variability and potential nonlinearity in the response of disease vectors to environmental change.     

产瓦伦西亚烯酿酒酵母的表达载体适配及发酵碳氮源优化

瓦伦西亚烯是一种倍半萜类化合物,广泛应用于香水、香皂、食品和饮料等工业制造上。但由于其自然含量极低,且目前获取瓦伦西亚烯的方法较为麻烦且花费高,因而构建细胞工厂进行瓦伦西亚烯的生物合成是更为高效和环保的方法。选取酿酒酵母(Saccharomyces cerevisiae)作为宿主构建细胞工厂,先在酿酒酵母基因组上引入黄扁柏的瓦伦西亚烯合成酶(Valencene synthase from Callitropsis nootkatensis,CnVS),实现瓦伦西亚烯的初步合成,初始产量为4.16 mg/L。随后利用CRISPR/Cas9系统对酿酒酵母中Mevalonate(MVA)途径的erg9和rox1基因进行敲除,提高通往瓦伦西亚烯合成的碳流量。不同碳氮源浓度发酵的结果表明,细胞生长积累过高可能不利于瓦伦西亚烯的积累。最后探究了不同CnVS表达载体对瓦伦西亚烯产量的影响,并获得17.54 mg/L的最高产量,是出发菌株的4.2倍。     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

ObjectivesMammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task.Material and methodsBasically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images.ResultsReported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate.ConclusionReported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.     

Efficient Editing of an Adenoviral Vector Genome with CRISPR/Cas9

Immunotherapy based on genetic modification of T cells has played an important role in the treatment of tumors and viral infections. Moreover, adenoviral vectors engineered with improved safety due to their inability to integrate into the host genome have been key in the clinical application of T cell therapy. However, the commonly used adenoviral vector Ad5 exhibits low efficiency of infection of human T cells and the details of the intracellular trafficking pathway of adenoviral vectors in human primary T cells remains unclear. Resolution of these issues will depend on successful modification of the adenoviral vector. To this end, here we describe the successful establishment of a simple and efficient method for editing adenoviral vectors in vitro using the CRISPR-Cas9 gene editing system to target the adenoviral fiber gene. Electronic supplementary materialThe online version of this article (10.1007/s12088-020-00905-3) contains supplementary material, which is available to authorized users.     

Genetic Analysis of Human Adenovirus Type 7 Strains Circulating in Different Parts of China

To investigate the molecular epidemiology and genetic variation of human adenovirus type 7(HAdV-7) in children with acute respiratory infections(ARI) in China. HAdV-7-positive respiratory samples collected from children with ARI in Beijing, Shijiazhuang, Wenzhou and Guangzhou from 2014–2018 were selected for gene amplification and sequence analysis. Fifty-seven HAdV-7 clinical strains with hexon, penton base and fiber gene sequences were obtained. Meanwhile17 strains were selected randomly from different cities for whole genome sequencing. Phylogenetic and variation analyses were performed based on the obtained sequences, HAdV-7 prototype strain Gomen(AY594255), vaccine strains(AY495969 and AY594256) and representative sequences of strains. The phylogenetic trees constructed based on whole genome sequences, major capsid protein genes(hexon, penton base and fiber) and the early genes(E1, E2, E3 and E4) were not completely consistent. The HAdV-7 strains obtained in this study always clustered with most of the circulating strains worldwide from the 1980 s to the present. Compared with the HAdV-7 prototype strain Gomen(AY594255), some amino acid mutations in loop1 and loop2 of hexon and the RGD loop region of the penton base gene were observed. Recombination analysis showed that partial regions of 55 k Da protein and 100 kDa hexon-assembly associated protein genes among all HAdV-7 strains in this study were from HAdV-16 and HAdV-3, respectively. Our study demonstrated the molecular evolution characteristics of HAdV-7 strains circulating in China and provided basic reference data for the prevention, control and vaccine development of HAdV-7.     

A Self-Biomineralized Novel Adenovirus Vectored COVID-19 Vaccine for Boosting Immunization of Mice

Virologica Sinica - SARS-CoV-2 has caused more than 3.8 million deaths worldwide, and several types of COVID-19 vaccines are urgently approved for use, including adenovirus vectored vaccines....     

Quality Control Testing,Characterization and Critical Quality Attributes of Adeno-Associated Virus Vectors Used for Human Gene Therapy

For adeno-associated virus (AAV)-based human gene therapy, challenges for the translation of promising research results to successful clinical development include optimization of vector design and manufacturing processes to ensure that vectors prepared for administration to human subjects have attributes consistent with safe and durable expression. This article briefly reviews quality control methods for routine testing and supplemental characterization of AAV vectors for investigational product development. The relationship of vector and manufacturing process design with product critical quality attributes is discussed.     

Active and machine learning-based approaches to rapidly enhance microbial chemical production

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.