一种经microRNA敲低PD-1的新型慢病毒载体在CAR-T细胞中的应用

本研究将microRNA插入EF1α启动子的内含子中,构建携带沉默PD-1基因的miRNA的新型慢病毒载体,并将其应用于CAR-T细胞。通过流式细胞术检测慢病毒载体转导效率和PD-1沉默效率;Westernblotting检测PD-1蛋白表达差异;荧光定量PCR检测microRNA相对表达情况;荧光素酶生物发光法和流式细胞术检测CAR-T细胞的能力。结果显示与U6转录microRNA的载体相比较,将microRNA插入到EF1-α内含子中的病毒载体转导效率更显著,对PD-1的敲低效率均达90%以上,且Westernblotting结果验证了PD-1的敲低效果。另外通过荧光定量PCR,可显示出转导该新型慢病毒载体的Jurkat细胞内microRNA的相对表达量。荧光素酶生物发光法证实了CAR-T细胞针对靶细胞的特异杀伤性,流式细胞术结果表明沉默PD-1的CAR-T细胞相较于正常CAR-T细胞显示出更强的特异性杀伤能力。本研究成功构建了经microRNA敲低PD-1的新型慢病毒载体并验证了其转导效率的优越性,以及基于此载体表达的microRNA可高效地沉默PD-1;且应用此载体的CAR-T细胞能发挥更强的杀伤活性,从而为后续该CAR-T细胞治疗表达PD-L1的肿瘤奠定基础。     

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.     

乳酸菌食品级表达载体的研究与应用

乳酸菌是能够发酵糖类产生大量有机酸的革兰氏阳性菌的通称,在发酵食品中有着悠久的应用历史。乳酸菌通常被认为是安全菌株,这些微生物的基因工程操作在食品、医学等方面具有广阔的应用前景。表达载体是基因工程中常用的工具之一,大多数乳酸菌的表达载体通常以抗生素抗性基因作为选择标记,然而抗性基因具有潜在的转移性,因此需要开发食品级表达载体。食品级表达载体不含有抗生素的抗性基因,仅包含来自同源宿主或通常被认为是安全生物的DNA。本文介绍了乳酸菌食品级表达载体的构成及其常用宿主,同时对乳酸菌食品级表达载体的应用进行了归纳总结。     

基于pDS132的基因敲除中创伤弧菌氯霉素的耐药性变化及影响

【背景】基于自杀载体的基因敲除在单基因敲除上的应用较为常见,但在多基因敲除过程中细菌耐药性的变化及对后续敲除的影响尚未明确。【目的】探究基于自杀载体pDS132的创伤弧菌vvhA与rtxA1双基因敲除株构建过程中,创伤弧菌对氯霉素耐药性的变化及对后续基因敲除的影响。【方法】基于自杀载体pDS132的同源重组法构建创伤弧菌YJ016的单基因敲除株YJ016-ΔvvhA、YJ016-ΔrtxA1和双基因敲除株YJ016-ΔvvhAΔrtxA1,记录单交换筛选时的氯霉素浓度与筛选平板上成功重组的菌株所占比例。琼脂稀释法测定野生型与敲除株的氯霉素最低抑菌浓度(Minimum InhibitoryConcentration,MIC)和抗性突变频率,纸片扩散法测定菌株对其他药物的敏感性,分析其对单交换筛选的影响。【结果】单基因敲除株的氯霉素MIC及氯霉素抗性突变频率高于野生型;双基因敲除株的庆大霉素抑菌圈直径小于野生型。单基因敲除时,单交换重组菌株的占比为100%(20/20);在YJ016-ΔvvhA上敲除rtxA1基因,氯霉素筛选浓度为2、4μg/mL时,单交换重组菌株的占比分别为40%(8/20)、5%(1/20);在YJ016-ΔrtxA1上敲除vvhA基因,氯霉素筛选浓度为2、4μg/mL时,单交换重组菌株的占比均为0%(0/20)。【结论】基于自杀载体pDS132的基因敲除中创伤弧菌氯霉素的耐药性升高,可能影响后续单交换重组的筛选,该结果为基于自杀载体的同源重组技术应用于多基因敲除提供了参考。     

乳酸菌作为药物分子粘膜投递载体的研究进展

乳酸菌是被公认为安全的食品级微生物,广泛地应用于食品生产、保存以及作为益生菌促进人类健康。鉴于发展有效的投递药物分子策略的需要,乳酸菌成为了极有吸引力的用于口服、鼻饲及阴道进行粘膜投递药物分子的活载体。用乳酸菌作为药物分子的投递载体,安全性好,且可直接合成并投递目标蛋白,显著降低药物生产成本。到目前为止,乳酸菌作为粘膜投递载体,已成功地向粘膜组织投递了一系列功能蛋白用以治疗多种疾病。文中综述了近20年的数据,重点聚焦乳酸菌作为药物分子投递载体的发展和应用,为今后乳酸菌作为活载体的临床研究提供一定参考。