Modular construction of mammalian gene circuits using TALE transcriptional repressors

An important goal of synthetic biology is the rational design and predictable implementation of synthetic gene circuits using standardized and interchangeable parts. However, engineering of complex circuits in mammalian cells is currently limited by the availability of well-characterized and orthogonal transcriptional repressors. Here, we introduce a library of 26 reversible transcription activator–like effector repressors (TALERs) that bind newly designed hybrid promoters and exert transcriptional repression through steric hindrance of key transcriptional initiation elements. We demonstrate that using the input-output transfer curves of our TALERs enables accurate prediction of the behavior of modularly assembled TALER cascade and switch circuits. We also show that TALER switches using feedback regulation exhibit improved accuracy for microRNA-based HeLa cancer cell classification versus HEK293 cells. Our TALER library is a valuable toolkit for modular engineering of synthetic circuits, enabling programmable manipulation of mammalian cells and helping elucidate design principles of coupled transcriptional and microRNA-mediated post-transcriptional regulation.National Institutes of Health (U.S.) (Grant 5R01CA155320-04)National Institutes of Health (U.S.) (Grant P50GM098792)National Institutes of Health (U.S.) (Grant 1R01CA173712-01

A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification

As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes

A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification

As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes

Comparative transcriptome analysis of the invasive weed Mikania micrantha with its native congeners provides insights into genetic basis underlying successful invasion

Abstract Background Mikania micrantha H.B.K. (Asteraceae) is one of the world’s most invasive weeds which has been rapidly expanding in tropical Asia, including China, while its close relative M. cordata, the only Mikania species native to China, shows no harm to the local ecosystems. These two species are very similar in morphology but differ remarkably in several ecological and physiological traits, representing an ideal system for comparative analysis to investigate the genetic basis underlying invasion success. In this study, we performed RNA-sequencing on the invader M. micrantha and its native congener M. cordata in China, to unravel the genetic basis underlying the strong invasiveness of M. micrantha. For a more robust comparison, another non-invasive congener M. cordifolia was also sequenced and compared. Results A total of 52,179, 55,835, and 52,983 unigenes were obtained for M. micrantha, M. cordata, and M. cordifolia, respectively. Phylogenetic analyses and divergence time dating revealed a relatively recent split between M. micrantha and M. cordata, i.e., approximately 4.81 million years ago (MYA), after their divergence with M. cordifolia (8.70 MYA). Gene ontology classifications, pathway assignments and differential expression analysis revealed higher representation or significant up-regulation of genes associated with photosynthesis, energy metabolism, protein modification and stress response in M. micrantha than in M. cordata or M. cordifolia. Analysis of accelerated evolution and positive selection also suggested the importance of these related genes and processes to the adaptability and invasiveness of M. micrantha. Particularly, most (77 out of 112, i.e. 68.75%) positively selected genes found in M. micrantha could be classified into four groups, i.e., energy acquisition and utilization (10 genes), growth and reproduction (13 genes), protection and repair (34 genes), and signal transduction and expression regulation (20 genes), which may have contributed to the high adaptability of M. micrantha to various new environments and the capability to occupy a wider niche, reflected in its high invasiveness. Conclusions We characterized the transcriptomes of the invasive species M. micrantha and its non-invasive congeners, M. cordata and M. cordifolia. A comparison of their transcriptomes provided insights into the genetic basis of the high invasiveness of M. micrantha

Associations between Inflammatory Cytokine Gene Polymorphisms and Susceptibilities to Intracranial Aneurysm in Chinese Population

Intracranial aneurysm (IA) is a complex disease caused by genetic and environmental factors. Evidence indicates that inflammation plays an important role in IA occurrence. We aimed to explore the associations between inflammatory cytokine gene polymorphisms and IA in a Chinese population. This study enrolled 768 participants of Han ethnicity, including 384 patients with IA and 384 healthy individuals. Sixteen single nucleotide polymorphisms (SNPs) of IL1, IL6, IL12, and TNF-α genes were genotyped using the Sequenom MassARRAY platform. Univariate and multivariate logistic regression analyses were used to analyze the associations. We found IL12B rs3181216 was significantly associated with IA in the recessive and additive models (OR=0.46, 95% CI = 0.23–0.89, P=0.022; OR=0.74, 95% CI = 0.56–0.98, P=0.034, respectively). TNF-α rs1799964 was associated with IA in dominant and additive models (OR=0.67, 95% CI = 0.46–0.98, P=0.041; OR=0.71, 95% CI = 0.51–0.98, P=0.034, respectively). IL1A rs17561 was associated with single IA susceptibility (dominant model: OR=0.52, 95% CI = 0.31–0.85, P=0.040). The IL12B rs3181216 polymorphism was associated with single IA susceptibility in the recessive model (OR=0.41, 95% CI = 0.18–0.93, P=0.033). The IL12B rs2195940 polymorphism was associated with multiple IAs susceptibility (dominant model: OR=0.28, 95% CI = 0.09–0.89, P=0.031; additive model: OR=0.28, 95% CI = 0.09–0.90, P=0.032). TNF-α rs1799964 was associated with multiple IAs susceptibility in the dominant model (OR=0.54, 95% CI = 0.30–0.97, P=0.040). No associations were found between other polymorphisms and IA susceptibility. Therefore, IL1A, IL12B, and TNF-α gene polymorphisms are associated with IA susceptibility in a Chinese population

Functional Characterization of Insulation Effect for Synthetic Gene Circuits in Mammalian Cells

Insulators are noncoding gene regulatory elements in eukaryotic genome, which function as chromatin partitioning boundaries, and block interference across different chromatin domains. To facilitate modular construction of synthetic gene circuit that is usually composed of multiple transcription cassettes, unwanted cross-regulations between different cassettes should be avoided. Here, we developed a quantitative method to characterize the functional effect of three insulators on the cross-regulations of six promoters in mammalian cells. We showed that the unwanted cross-regulations displayed a threshold-like effect, and the threshold position varied along with the context of promoters and insulators. We tested the function of insulators in both cascade and sensory switch circuits assembled in episomal plasmid vectors, and showed that the insulation effect was mainly revealed on the first regulatory layer of the cascade circuit. A deviation on the response curve of the sensory switch circuit with or without insulators was observed, but response intensity of some sensory switch circuits were not affected. Therefore, our results provided a general guide on the selection of insulators with varying promoters in episomal synthetic gene circuits in mammalian cells, which may be useful to reduce the effect of the unwanted cross-regulations

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Additional file 3: of Comparative transcriptome analysis of the invasive weed Mikania micrantha with its native congeners provides insights into genetic basis underlying successful invasion

Assessment of per-base sequence accuracy of M. micrantha, M. cordata, and M. cordifolia unigenes. (a) Mapping depth distribution of assembled unigene sequences. Histogram shows the percentages of bases with certain ranges of coverage depth. (b) Identity distribution between assembled unigenes and their corresponding sequences from public databases. (PDF 132 kb