Overexpression of ER Protein Processing and Apoptosis Regulator Genes in Human Embryonic Kidney 293 Cells Improves Gene Therapy Vectors Production

Gammaretroviral and lentiviral vectors (γ‐RV and LV) are among the most used vectors in gene therapy. Currently, human embryonic kidney (HEK) 293 cells, the manufacture platform of choice for these vectors, provide low transducing particle yields, challenging their therapeutic applications and commercialization. This work studies metabolic pathways, focusing on endoplasmic reticulum (ER) protein processing and anti‐apoptotic mechanisms, influencing vector productivity in HEK 293 cell substrates. To that end, four candidate genes—protein disulfide isomerase family A member 2 gene, heat shock protein family A (Hsp70) member 5 gene, X‐box binding protein 1 gene (ER protein processing), and B‐cell lymphoma 2 protein gene (anti‐apoptotic)—are individually stably expressed in the cells. How their overexpression level influence vector yields is analyzed by establishing cell populations with incremental genomic copies of each. γ‐RV volumetric productivity increases up to 97% when overexpressing ER protein processing genes. LV volumetric production increases 53% when overexpressing the anti‐apoptotic gene. Improvements are associated with higher cell specific productivities and dependent on gene overexpression level, highlighting the importance of fine‐tuning gene expression. Overall, this work discloses gene engineering targets enabling efficient gene therapy product manufacture showing that ER protein processing and anti‐apoptotic pathways are pivotal to producer cell performance.     

Improving NK1R-targeted gene delivery of stearyl-antimicrobial peptide CAMEL by conjugating it with substance P

Specificity is a crucial condition that hampers the application of non-viral vectors for cancer gene therapy. In a previous study, we developed an efficient gene vector, stearyl-CAMEL, using N-terminal stearylation of the antimicrobial peptide CAMEL. Substance P (SP), an 11-residue neuropeptide, rapidly enters cells after binding to the neurokinin-1 receptor (NK1R), which is expressed in many cancer cell lines. In this study, the NK1R-targeted gene vector stearyl-CMSP was constructed by conjugating SP to the C-terminus of stearyl-CAMEL. Our results indicated that stearyl-CMSP displayed significant transfection specificity for NK1R-expressing cells compared with that shown by stearyl-CAMEL. Accordingly, the stearyl-CMSP/p53 plasmid complexes had significantly higher antiproliferative activity against HEK293-NK1R cells than they did against HEK293 cells, while the stearyl-CAMEL/p53 plasmid complexes did not show this specificity in antiproliferative activity. Consequently, conjugation of the NK1R-targeted ligand SP is a simple and successful strategy to construct efficient cancer-targeted non-viral gene vectors.     

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。     

Interactions among Triatoma sanguisuga blood feeding sources,gut microbiota and Trypanosoma cruzi diversity in southern Louisiana

Integrating how biodiversity and infectious disease dynamics are linked at multiple levels and scales is highly challenging. Chagas disease is a vector‐borne disease, with specificities of the triatomine vectors and Trypanosoma cruzi parasite life histories resulting in a complex multihost and multistrain life cycle. Here, we tested the hypothesis that T. cruzi transmission cycles are shaped by triatomine host communities and gut microbiota composition by comparing the integrated interactions of Triatoma sanguisuga in southern Louisiana with feeding hosts, T. cruzi parasite and bacterial microbiota in two habitats. Bugs were collected from resident's houses and animal shelters and analysed for genetic structure, blood feeding sources, T. cruzi parasites, and bacterial diversity by PCR amplification of specific DNA markers followed by next‐generation sequencing, in an integrative metabarcoding approach. T. sanguisuga feeding host communities appeared opportunistic and defined by host abundance in each habitat, yielding distinct parasite transmission networks among hosts. The circulation of a large diversity of T. cruzi DTUs was also detected, with TcII and TcV detected for the first time in triatomines in the US. The bacterial microbiota was highly diverse and varied significantly according to the DTU infecting the bugs, indicating specific interactions among them in the gut. Expanding such studies to multiple habitats and additional triatomine species would be key to further refine our understanding of the complex life cycles of multihost, multistrain parasites such as T. cruzi, and may lead to improved disease control strategies.     

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.     

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.