Isolation and Characterization of an LBD Transcription Factor CsLBD39 from Tea Plant (Camellia sinensis) and Its Roles in Modulating Nitrate Content by Regulating Nitrate-Metabolism-Related Genes

Nitrate nitrogen is an important nitrogen source for tea plants’ growth and development. LBD transcription factors play important roles in response to the presence of nitrate in plants. The functional study of LBD transcription factors in tea plants remains limited. In this study, the LBD family gene CsLBD39 was isolated and characterized from tea plants. Sequence analysis indicated that CsLBD39 contained a highly conserved CX₂CX₆CX₃CX domain. The phylogenetic tree assay showed that CsLBD39 belonged to class II subfamily of the LBD family. CsLBD39 was highly expressed in flowers and root; we determined that its expression could be induced by nitrate treatment. The CsLBD39 protein was located in the nucleus and has transcriptional activation activity in yeast. Compared with the wild type, overexpression of CsLBD39 gene in Arabidopsis resulted in smaller rosettes, shorter main roots, reduced lateral roots and lower plant weights. The nitrate content and the expression levels of genes related to nitrate transport and regulation were decreased in transgenic Arabidopsis hosting CsLBD39 gene. Compared with the wild type, CsLBD39 overexpression in transgenic Arabidopsis had smaller cell structure of leaves, shorter diameter of stem cross section, and slender and compact cell of stem longitudinal section. Under KNO₃ treatment, the contents of nitrate, anthocyanins, and chlorophyll in leaves, and the content of nitrate in roots of Arabidopsis overexpressing CsLBD39 were reduced, the expression levels of nitrate transport and regulation related genes were decreased. The results revealed that CsLBD39 may be involved in nitrate signal transduction in tea plants as a negative regulator and laid the groundwork for future studies into the mechanism of nitrate response.

Protein subcellular localization based on deep image features and criterion learning strategy

The spatial distribution of proteome at subcellular levels provides clues for protein functions, thus is important to human biology and medicine. Imaging-based methods are one of the most important approaches for predicting protein subcellular location. Although deep neural networks have shown impressive performance in a number of imaging tasks, its application to protein subcellular localization has not been sufficiently explored. In this study, we developed a deep imaging-based approach to localize the proteins at subcellular levels. Based on deep image features extracted from convolutional neural networks (CNNs), both single-label and multi-label locations can be accurately predicted. Particularly, the multi-label prediction is quite a challenging task. Here we developed a criterion learning strategy to exploit the label-attribute relevancy and label-label relevancy. A criterion that was used to determine the final label set was automatically obtained during the learning procedure. We concluded an optimal CNN architecture that could give the best results. Besides, experiments show that compared with the hand-crafted features, the deep features present more accurate prediction with less features. The implementation for the proposed method is available at https://github.com/RanSuLab/ProteinSubcellularLocation.

The ascorbate peroxidase 1 regulates ascorbic acid metabolism in fresh-cut leaves of tea plant during postharvest storage under light/dark conditions

Postharvest storage conditions affect the ascorbic acid (AsA) levels in fresh-cut leaves of horticultural crops. However, the detailed mechanism of AsA metabolism in the fresh-cut leaves of tea plant (Camellia sinensis) during postharvest storage under light/dark conditions remains unclear. To investigate the AsA mechanism, we treated fresh-cut tea leaves with light/dark during postharvest storage. An ascorbate peroxidase 1 (CsAPX1) protein involved in AsA metabolism was identified by iTRAQ analysis. Gene expression profile of CsAPX1 encoding ascorbate peroxidase (APX) was regulated by light/dark conditions. AsA accumulation and APX activity were suppressed by light/dark conditions. SDS-PAGE analysis showed that the molecular mass of recombinant CsAPX1 protein was about 34.45 kDa. Subcellular localization indicated that CsAPX1 protein was a cytosol ascorbate peroxidase. Overexpression CsAPX1 in Arabidopsis indicated that the decrease of AsA content and APX activity in transgenic lines were less significant than that of WT during postharvest storage under light/dark conditions. These data suggested that CsAPX1 involved in regulating AsA metabolism through effecting on the changes of AsA accumulation and APX activity in fresh-cut tea leaves during postharvest storage under light/dark conditions.

MIC_Locator: a novel image-based protein subcellular location multi-label prediction model based on multi-scale monogenic signal representation and intensity encoding strategy

Background

Protein subcellular localization plays a crucial role in understanding cell function. Proteins need to be in the right place at the right time, and combine with the corresponding molecules to fulfill their functions. Furthermore, prediction of protein subcellular location not only should be a guiding role in drug design and development due to potential molecular targets but also be an essential role in genome annotation. Taking the current status of image-based protein subcellular localization as an example, there are three common drawbacks, i.e., obsolete datasets without updating label information, stereotypical feature descriptor on spatial domain or grey level, and single-function prediction algorithm’s limited capacity of handling single-label database.

Results

In this paper, a novel human protein subcellular localization prediction model MIC_Locator is proposed. Firstly, the latest datasets are collected and collated as our benchmark dataset instead of obsolete data while training prediction model. Secondly, Fourier transformation, Riesz transformation, Log-Gabor filter and intensity coding strategy are employed to obtain frequency feature based on three components of monogenic signal with different frequency scales. Thirdly, a chained prediction model is proposed to handle multi-label instead of single-label datasets. The experiment results showed that the MIC_Locator can achieve 60.56% subset accuracy and outperform the existing majority of prediction models, and the frequency feature and intensity coding strategy can be conducive to improving the classification accuracy.

Conclusions

Our results demonstrate that the frequency feature is more beneficial for improving the performance of model compared to features extracted from spatial domain, and the MIC_Locator proposed in this paper can speed up validation of protein annotation, knowledge of protein function and proteomics research.

Determining the subcellular location of new proteins from microscope images using local features

MOTIVATION

Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified.

RESULTS

Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets.

AVAILABILITY

The datasets are available for download at http://murphylab.web.cmu.edu/data/. The software was written in Python and C++ and is available under an open-source license at http://murphylab.web.cmu.edu/software/. The code is split into a library, which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address.

CONTACT

murphy@cmu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores

Motivation: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut—normalized cut prime (FABS-NC^′), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.

Results: We compare the performance of FABS-NC^′ to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC^′ also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC^′ consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC^′: In some cases FABS-NC^′ produces over half correctly predicted ranking experiment trials than FABS-SVM.

Availability: The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.

Contact:yxy128@berkeley.edu

Ensemble of Neural Networks for Automated Cell Phenotype Image Classification

Subcellular location is related to the knowledge of the spatial distribution of a protein within the cell. The knowledge of the location of all proteins is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. This chapter focuses on the study of machine learning techniques for cell phenotype image classification and is aimed at pointing out some of the advantages of using a multi-classifier system instead of a stand-alone method to solve this difficult classification problem. The main problems and solutions proposed in this field are discussed and a new approach is proposed based on ensemble of neural networks trained by local and global features. Finally, the most used benchmarks for this problem are presented and an experimental comparison among several state-of-the-art approaches is reported which allows to quantify the performance improvement obtained by the approach proposed in this chapter.

A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images

Motivation: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise.

Results: We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A.

Availability: Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/.

Contact:chunnan@iis.sinica.edu.tw

Supplementary information:Supplementary data are available at Bioinformatics online.

Screening cellular feature measurements for image-based assay development

The typical “design” approach to image-based assay development involves choosing measurements that are likely to correlate with the phenotype of interest, based on the researcher’s intuition and knowledge of image analysis. An alternate “screening” approach is to measure a large number of cellular features and systematically test each feature to identify those that are best able to distinguish positive and negative controls while taking precautions to avoid overfitting the available data. The cell measurement software the authors previously developed, CellProfiler, makes both approaches straightforward, easing the process of assay development. Here, they demonstrate the use of the screening approach to image assay development to select the best measures for scoring publicly available image sets of 2 cytoplasm-to-nucleus translocation assays and 2 Transfluor assays. The authors present the resulting assay quality measures as a baseline for future algorithm comparisons, and all software, methods, and images they present are freely available.

Local binary patterns variants as texture descriptors for medical image analysis

OBJECTIVE

This paper focuses on the use of image-based machine learning techniques in medical image analysis. In particular, we present some variants of local binary patterns (LBP), which are widely considered the state of the art among texture descriptors. After we provide a detailed review of the literature about existing LBP variants and discuss the most salient approaches, along with their pros and cons, we report new experiments using several LBP-based descriptors and propose a set of novel texture descriptors for the representation of biomedical images. The standard LBP operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Our variants are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. These sets of features are then used for training a machine-learning classifier (a stand-alone support vector machine).

METHODS AND MATERIALS

Extensive experiments are conducted using the following three datasets:

RESULTS AND CONCLUSION

Our results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets. EQP is based on an elliptic neighborhood and a 5 levels scale for encoding the local gray-scale difference. Particularly interesting are the results on the widely studied 2D-HeLa dataset, where, to the best of our knowledge, the proposed descriptor obtains the highest performance among all the several texture descriptors tested in the literature.

Novel features for automated cell phenotype image classification

The most common method of handling automated cell phenotype image classification is to determine a common set of optimal features and then apply standard machine-learning algorithms to classify them. In this chapter, we use advanced methods for determining a set of optimized features for training an ensemble using random subspace with a set of Levenberg-Marquardt neural networks. The process requires that we first run several experiments to determine the individual features that offer the most information. The best performing features are then concatenated and used in the ensemble classification. Applying this approach, we have obtained an average accuracy of 97.4% using the three best benchmarks for this problem: the 2D HeLa dataset and both the endogenous and the transfected LOCATE mouse protein subcellular localization databases.