Turning the tide against TB: Remaking ineffective host defenses into mechanisms for tuberculosis control

Most antibiotics, including the drugs currently used for treating tuberculosis (TB), were first discovered as molecules that inhibit bacterial growth in laboratory culture conditions and later translated to infection models and clinical use. Mycobacterium tuberculosis (Mtb) has evolved specifically to survive in its human host, and it is in this infectious context that new drugs need to work. The host environment is characterized by a multitude of antimicrobial defenses induced by the immune system, and we can leverage these defenses to kill Mtb in vivo. Mtb employs a diverse set of responses to survive host defenses. By blocking these responses, we can make Mtb more susceptible to host immunity, turning these previously impotent defenses into effective strategies of immune control

High-resolution definition of the Vibrio cholerae essential gene set with hidden Markov model–based analyses of transposon-insertion sequencing data

The coupling of high-density transposon mutagenesis to high-throughput DNA sequencing (transposon-insertion sequencing) enables simultaneous and genome-wide assessment of the contributions of individual loci to bacterial growth and survival. We have refined analysis of transposon-insertion sequencing data by normalizing for the effect of DNA replication on sequencing output and using a hidden Markov model (HMM)-based filter to exploit heretofore unappreciated information inherent in all transposon-insertion sequencing data sets. The HMM can smooth variations in read abundance and thereby reduce the effects of read noise, as well as permit fine scale mapping that is independent of genomic annotation and enable classification of loci into several functional categories (e.g. essential, domain essential or ‘sick’). We generated a high-resolution map of genomic loci (encompassing both intra- and intergenic sequences) that are required or beneficial for in vitro growth of the cholera pathogen, Vibrio cholerae. This work uncovered new metabolic and physiologic requirements for V. cholerae survival, and by combining transposon-insertion sequencing and transcriptomic data sets, we also identified several novel noncoding RNA species that contribute to V. cholerae growth. Our findings suggest that HMM-based approaches will enhance extraction of biological meaning from transposon-insertion sequencing genomic data

ARTIST: High-Resolution Genome-Wide Assessment of Fitness Using Transposon-Insertion Sequencing

Transposon-insertion sequencing (TIS) is a powerful approach for deciphering genetic requirements for bacterial growth in different conditions, as it enables simultaneous genome-wide analysis of the fitness of thousands of mutants. However, current methods for comparative analysis of TIS data do not adjust for stochastic experimental variation between datasets and are limited to interrogation of annotated genomic elements. Here, we present ARTIST, an accessible TIS analysis pipeline for identifying essential regions that are required for growth under optimal conditions as well as conditionally essential loci that participate in survival only under specific conditions. ARTIST uses simulation-based normalization to model and compensate for experimental noise, and thereby enhances the statistical power in conditional TIS analyses. ARTIST also employs a novel adaptation of the hidden Markov model to generate statistically robust, high-resolution, annotation-independent maps of fitness-linked loci across the entire genome. Using ARTIST, we sensitively and comprehensively define Mycobacterium tuberculosis and Vibrio cholerae loci required for host infection while limiting inclusion of false positive loci. ARTIST is applicable to a broad range of organisms and will facilitate TIS-based dissection of pathways required for microbial growth and survival under a multitude of conditions

Plasma Membrane Profiling Reveals Upregulation of ABCA1 by Infected Macrophages Leading to Restriction of Mycobacterial Growth.

The plasma membrane represents a critical interface between the internal and extracellular environments, and harbors multiple proteins key receptors and transporters that play important roles in restriction of intracellular infection. We applied plasma membrane profiling, a technique that combines quantitative mass spectrometry with selective cell surface aminooxy-biotinylation, to Bacille Calmette-Guérin (BCG)-infected THP-1 macrophages. We quantified 559 PM proteins in BCG-infected THP-1 cells. One significantly upregulated cell-surface protein was the cholesterol transporter ABCA1. We showed that ABCA1 was upregulated on the macrophage cell-surface following infection with pathogenic mycobacteria and knockdown of ABCA1 resulted in increased mycobacterial survival within macrophages, suggesting that it may be a novel mycobacterial host-restriction factor.Medical Research Council (Clinician Scientist Fellowship), Tsinghua University, Wellcome Trust (Senior Fellowship (Grant ID: 108070/Z/15/Z)), National Institute for Health Research (Academic Clinical Fellowship), China Scholarship CouncilThis is the final version of the article. It first appeared from Frontiers via http://dx.doi.org/10.3389/fmicb.2016.0108

High-Throughput Sequencing Enhanced Phage Display Identifies Peptides That Bind Mycobacteria

Bacterial cell wall components have been previously used as infection biomarkers detectable by antibodies. However, it is possible that the surface of the Mycobacterium tuberculosis (M. tb), the causative agent of tuberculosis (TB), also possesses molecules which might be non-antigenic. This makes the probing of biomarkers on the surface of M. tb cell wall difficult using antibodies. Here we demonstrate the use of phage display technology to identify peptides that bind to mycobacteria. We identified these clones using both random clone picking and high throughput sequencing. We demonstrate that random clone picking does not necessarily identify highly enriched clones. We further showed that the clone displaying the CPLHARLPC peptide which was identified by Illumina sequencing as the most enriched, binds better to mycobacteria than three clones selected by random picking. Using surface plasmon resonance, we showed that chemically synthesised CPLHARLPC peptide binds to a 15 KDa peptide from M.tb H37Rv whole cell lysates. These observations demonstrate that phage display technology combined with high-throughput sequencing is a powerful tool to identify peptides that can be used for investigating potential non-antigenic biomarkers for TB and other bacterial infections

Geometric Over-Constraints Detection: A Survey

Currently, geometric over-constraints detection is of major interest in several diferent felds. In terms of product development process (PDP), many approaches exist to compare and detect geometric over-constraints, to decompose geometric systems, to solve geometric constraints systems. However, most approaches do not take into account the key characteristics of a geometric system, such as types of geometries, diferent levels at which a system can be decomposed e.g numerical or structural. For these reasons, geometric over-constraints detection still faces challenges to fully satisfy real needs of engineers. The aim of this paper is to review the state-of-the-art of works involving with geometric over-constraints detection and to identify pos sible research directions. Firstly, the paper highlights the user requirements for over-constraints detection when modeling geometric constraints systems in PDP and proposes a set of criteria to analyze the available methods classifed into four categories: level of detecting over-constraints, system decomposition, system modeling and results generation. Secondly, it introduces and analyzes the available methods by grouping them based on the introduced criteria. Finally, it discusses pos sible directions and future challenges

Ectopic tissue engineered ligament with silk collagen scaffold for ACL regeneration: A preliminary study

Anterior cruciate ligament (ACL) reconstruction remains a formidable clinical challenge because of the lack of vascularization and adequate cell numbers in the joint cavity. In this study, we developed a novel strategy to mimic the early stage of repair in vivo, which recapitulated extra-articular inflammatory response to facilitate the early ingrowth of blood vessels and cells. A vascularized ectopic tissue engineered ligament (ETEL) with silk collagen scaffold was developed and then transferred to reconstruct the ACL in rabbits without interruption of perfusion. At 2 weeks after ACL reconstruction, more well-perfused cells and vessels were found in the regenerated ACL with ETEL, which decreased dramatically at the 4 and 12 week time points with collagen deposition and maturation. ACL treated with ETEL exhibited more mature ligament structure and enhanced ligament-bone healing post-reconstructive surgery at 4 and 12 weeks, as compared with the control group. In addition, the ETEL group was demonstrated to have higher modulus and stiffness than the control group significantly at 12 weeks post-reconstructive surgery. In conclusion, our results demonstrated that the ETEL can provide sufficient vascularity and cellularity during the early stages of healing, and subsequently promote ACL regeneration and ligament-bone healing, suggesting its clinic use as a promising therapeutic modality. Statement of Significance Early inflammatory cell infiltration, tissue and vessels ingrowth were significantly higher in the extra articular implanted scaffolds than theses in the joint cavity. By mimicking the early stages of wound repair, which provided extra-articular inflammatory stimulation to facilitate the early ingrowth of blood vessels and cells, a vascularized ectopic tissue engineered ligament (ETEL) with silk collagen scaffold was constructed by subcutaneous implantation for 2 weeks. The fully vascularized TE ligament was then transferred to rebuild ACL without blood perfusion interruption, and was demonstrated to exhibit improved ACL regeneration, bone tunnel healing and mechanical properties. (C) 2017 Published by Elsevier Ltd on behalf of Acta Materialia Inc

Online and Adjusted Human Activities Recognition with Statistical Learning

Wearable human activity recognition (HAR) is a widely application system for our daily life. It hasbeen built in many devices, such as smartphone, smartwatch, activity tracker, and health monitor. Many researchers try to develop a system which requires less memory space and power, but has fast and accurate classification results. Moreover, the objective of adjusting the classifier by the system self is also a study direction. In the present study, we introduced the machine learning methods to both smartphone data and smartwatch data and an adjusted model with the continuous generating data. Further, we also proposed a new HAR system which could adjust it by customer’s personal input. First, we present a comparison with several popular machine learning methods using smartphone data and try to find the most effective model for identifying activities, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Decision Tree (DT), and so forth. Two datasets with different transition methods from smartphone are used. Also, we used grid search, multi-fold cross validation, and dimensional reduction method to improve the performance. Meanwhile, a two-layer method for activity identification is proposed. This method is more flexible of choosing classifiers for activities. Then, in order to avoid the fixed built in HAR system, we used an online method called Very Fast Decision Tree (VFDT) to mimic the real scenario, since we do not have data collected in a streaming basis. There are two main improvements from the existing models: 1) we train the model online and use the training data for training and adjusting then delete the previous data; 2) after building VFDT, the model can be adjusted to identify new activities by adding only small amount of labeled observations. Finally, we proposed a personalized HAR system with interactive function. With this system, customers could build their personal HAR system by inputting their data. The system includes two steps, unsupervised method and supervised method. The unsupervised step is used for identifying if the new input data has any new activities. K-cluster is applied. The supervised step is used for identifying the specific activities and update the activity classes if there are new activities. Quadratic discriminant analysis (QDA) is applied

Online and Adjusted Human Activities Recognition with Statistical Learning

Wearable human activity recognition (HAR) is a widely application system for our daily life. It hasbeen built in many devices, such as smartphone, smartwatch, activity tracker, and health monitor. Many researchers try to develop a system which requires less memory space and power, but has fast and accurate classification results. Moreover, the objective of adjusting the classifier by the system self is also a study direction. In the present study, we introduced the machine learning methods to both smartphone data and smartwatch data and an adjusted model with the continuous generating data. Further, we also proposed a new HAR system which could adjust it by customer’s personal input. First, we present a comparison with several popular machine learning methods using smartphone data and try to find the most effective model for identifying activities, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Decision Tree (DT), and so forth. Two datasets with different transition methods from smartphone are used. Also, we used grid search, multi-fold cross validation, and dimensional reduction method to improve the performance. Meanwhile, a two-layer method for activity identification is proposed. This method is more flexible of choosing classifiers for activities. Then, in order to avoid the fixed built in HAR system, we used an online method called Very Fast Decision Tree (VFDT) to mimic the real scenario, since we do not have data collected in a streaming basis. There are two main improvements from the existing models: 1) we train the model online and use the training data for training and adjusting then delete the previous data; 2) after building VFDT, the model can be adjusted to identify new activities by adding only small amount of labeled observations. Finally, we proposed a personalized HAR system with interactive function. With this system, customers could build their personal HAR system by inputting their data. The system includes two steps, unsupervised method and supervised method. The unsupervised step is used for identifying if the new input data has any new activities. K-cluster is applied. The supervised step is used for identifying the specific activities and update the activity classes if there are new activities. Quadratic discriminant analysis (QDA) is applied