Chuang Ma

The Reference Genome of the Halophytic Plant Eutrema salsugineum

Halophytes are plants that can naturally tolerate high concentrations of salt in the soil, and their tolerance to salt stress may occur through various evolutionary and molecular mechanisms. Eutrema salsugineum is a halophytic species in the Brassicaceae that can naturally tolerate multiple types of abiotic stresses that typically limit crop productivity, including extreme salinity and cold. It has been widely used as a laboratorial model for stress biology research in plants. Here, we present the reference genome sequence (241 Mb) of E. salsugineum at 8× coverage sequenced using the traditional Sanger sequencing-based approach with comparison to its close relative Arabidopsis thaliana. The E. salsugineum genome contains 26,531 protein-coding genes and 51.4% of its genome is composed of repetitive sequences that mostly reside in pericentromeric regions. Comparative analyses of the genome structures, protein-coding genes, microRNAs, stress-related pathways, and estimated translation efficiency of proteins between E. salsugineum and A. thaliana suggest that halophyte adaptation to environmental stresses may occur via a global network adjustment of multiple regulatory mechanisms. The E. salsugineum genome provides a resource to identify naturally occurring genetic alterations contributing to the adaptation of halophytic plants to salinity and that might be bioengineered in related crop species.

RNA Sequencing of Laser-Capture Microdissected Compartments of the Maize Kernel Identifies Regulatory Modules Associated with Endosperm Cell Differentiation

RNA profiling of maize kernel compartments revealed coexpression modules for each major cell type in the endosperm, including a module regulating differentiation of the basal endosperm transfer layer. Endosperm is an absorptive structure that supports embryo development or seedling germination in angiosperms. The endosperm of cereals is a main source of food, feed, and industrial raw materials worldwide. However, the genetic networks that regulate endosperm cell differentiation remain largely unclear. As a first step toward characterizing these networks, we profiled the mRNAs in five major cell types of the differentiating endosperm and in the embryo and four maternal compartments of the maize (Zea mays) kernel. Comparisons of these mRNA populations revealed the diverged gene expression programs between filial and maternal compartments and an unexpected close correlation between embryo and the aleurone layer of endosperm. Gene coexpression network analysis identified coexpression modules associated with single or multiple kernel compartments including modules for the endosperm cell types, some of which showed enrichment of previously identified temporally activated and/or imprinted genes. Detailed analyses of a coexpression module highly correlated with the basal endosperm transfer layer (BETL) identified a regulatory module activated by MRP-1, a regulator of BETL differentiation and function. These results provide a high-resolution atlas of gene activity in the compartments of the maize kernel and help to uncover the regulatory modules associated with the differentiation of the major endosperm cell types.

Dynamic Expression of Imprinted Genes Associates with Maternally Controlled Nutrient Allocation during Maize Endosperm Development

Genomic imprinting refers to the differential expression of parental alleles in a parent-of-origin manner. Through a genome-wide identification of the imprinted genes in hybrid maize endosperm, this work provides evidence that the allele-specific expression status of the most imprinted genes is subject to dynamic change associated with different developmental events in the maize endosperm. In angiosperms, the endosperm provides nutrients for embryogenesis and seed germination and is the primary tissue where gene imprinting occurs. To identify the imprintome of early developing maize (Zea mays) endosperm, we performed high-throughput transcriptome sequencing of whole kernels at 0, 3, and 5 d after pollination (DAP) and endosperms at 7, 10, and 15 DAP, using B73 by Mo17 reciprocal crosses. We observed gradually increased expression of paternal transcripts in 3- and 5-DAP kernels. In 7-DAP endosperm, the majority of the genes tested reached a 2:1 maternal versus paternal ratio, suggesting that paternal genes are nearly fully activated by 7 DAP. A total of 116, 234, and 63 genes exhibiting parent-specific expression were identified at 7, 10, and 15 DAP, respectively. The largest proportion of paternally expressed genes was at 7 DAP, mainly due to the significantly deviated parental allele expression ratio of these genes at this stage, while nearly 80% of the maternally expressed genes (MEGs) were specific to 10 DAP and were primarily attributed to sharply increased expression levels compared with the other stages. Gene ontology enrichment analysis of the imprinted genes suggested that 10-DAP endosperm-specific MEGs are involved in nutrient uptake and allocation and the auxin signaling pathway, coincident with the onset of starch and storage protein accumulation.

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

This work presents a machine learning–based method for transcriptome analysis via comparison of gene coexpression networks, which outperforms traditional statistical tests at identifying stress-related genes. Analysis of an Arabidopsis stress expression data set led to the prediction of candidate stress-related genes showing expression and network changes in response to multiple abiotic stresses. Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive “noninformative” genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained “informative” genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey’s biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

Position-specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures

It has been many years since position‐specific residue preference around the ends of a helix was revealed. However, all the existing secondary structure prediction methods did not exploit this preference feature, resulting in low accuracy in predicting the ends of secondary structures. In this study, we collected a relatively large data set consisting of 1860 high‐resolution, non‐homology proteins from the PDB, and further analyzed the residue distributions around the ends of regular secondary structures. It was found that there exist position‐specific residue preferences (PSRP) around the ends of not only helices but also strands. Based on the unique features, we proposed a novel strategy and developed a tool named E‐SSpred that treats the secondary structure as a whole and builds models to predict entire secondary structure segments directly by integrating relevant features. In E‐SSpred, the support vector machine (SVM) method is adopted to model and predict the ends of helices and strands according to the unique residue distributions around them. A simple linear discriminate analysis method is applied to model and predict entire secondary structure segments by integrating end‐prediction results, tri‐peptide composition, and length distribution features of secondary structures, as well as the prediction results of the most famous program PSIPRED. The results of fivefold cross‐validation on a widely used data set demonstrate that the accuracy of E‐SSpred in predicting ends of secondary structures is about 10% higher than PSIPRED, and the overall prediction accuracy (Q3 value) of E‐SSpred (82.2%) is also better than PSIPRED (80.3%). The E‐SSpred web server is available at http://bioinfo.hust.edu.cn/bio/tools/E‐SSpred/index.html.

Inhibitors targeting the influenza virus hemagglutinin.

The annual flu season causes thousands of deaths and millions of hospitalizations, which pose a great burden to global health and economy. Moreover, a flu pandemic arising from reassortment viruses, such as H5N1 and H1N1, raises even greater concern due to the lack of effective vaccines at the initial stage of flu outbreak. The influenza virus is the causative agent of flu infection. Currently there are four drugs in use to combat influenza infection. Amantadine and rimantadine are M2 proton channel blockers that inhibit virus uncoating; oseltamivir and zanamivir are neuraminidase (NA) inhibitors that inhibit virus release. However, recent years have witnessed a drastic increase in instances of drug resistance, and flu strains that are resistant to both classes of drugs have been reported. Thus, there is a pressing need to develop the next generation of anti-influenza drugs. Among a handful of anti-influenza drug targets, the viral fusion protein hemagglutinin (HA) is one of the most advanced. This review discusses the biological roles of HA during viral replication and highlights peptide- and small molecule-based HA inhibitors, including recent computationally designed HA binders. The text is organized into four sections based on the maturation stages of HA: inhibitors targeting the glycosylation of HA, the proteolytic activation of HA, the attachment of HA to host cell receptors, and peptide- and small molecule-based inhibitors targeting HA-mediated membrane fusion. Of particular interest are advances in the areas of developing dual inhibitors targeting both HA and NA and broad-spectrum HA inhibitors targeting both groups of HAs.

Feature Mining and Integration for Improving the Prediction Accuracy of Translation Initiation Sites in Eukaryotic mRNAs

Accurate prediction of translation initiation sites (TISs) is important for the annotation of genomes. Although many methods have been proposed to solve this problem, the prediction accuracy is still limited. In this paper, the features that have been widely used for predicting TISs are further analyzed, and it is found that some features of TISs and non-TISs are heavily dependent on the C+G content of sequences around AUG codons, and some features are quite different for non-TISs located in untranslated regions and coding regions considering different reading frames. Further, the strategy of using multiple support vector machines to fully make use of the information is proposed, and a new program TISKey for the prediction of TISs is developed. Testing results on widely used dataset demonstrate that TISKey could get better prediction accuracy. TISKey can be accessed at http://infosci.hust.edu.cn

A systems approach to a spatio-temporal understanding of the drought stress response in maize

Crops are often subjected to periods of drought stress during their life cycle. However, how stress response mechanisms contribute to the crosstalk between stress signaling pathways and developmental signaling pathways is still unknown. We built a gene co-expression network from a spatio-temporal transcriptomic map of the drought stress response in maize (Zea mays), profiled from three tissues and four developmental stages and characterized hub genes associated with duplication events, selection, and regulatory networks. Co-expression analysis grouped drought-response genes into ten modules, covering 844 highly connected genes (hub genes). Of these, 15.4% hub genes had diverged by whole-genome duplication events and 2.5% might then have been selected during natural domestication and artificial improvement processes, successively. We identified key transcription factor hubs in a transcriptional regulatory network, which may function as a crosstalk mechanism between drought stress and developmental signalling pathways in maize. Understanding the evolutionary biases that have evolved to enhance drought adaptation lays the foundation for further dissection of crosstalk between stress signalling pathways and developmental signalling pathways in maize, towards molecular design of new cultivars with desirable yield and greater stress tolerance.

miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences

MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify primary miRNAs and precursor miRNAs (pre-miRNAs), very few provide the functionality of locating mature miRNAs within plant pre-miRNAs. This manuscript introduces a novel algorithm for predicting miRNAs named miRLocator, which isbased on machine learning techniques and sequence and structural features extracted from miRNA:miRNA* duplexes. To address the class imbalance problem (few real miRNAs and a large number of pseudo miRNAs), the prediction models in miRLocator were optimized by considering critical (and often ignored) factors that can markedly affect the prediction accuracy of mature miRNAs, including the machine learning algorithm and the ratio between training positive and negative samples. Ten-fold cross-validation on 5854 experimentally validated miRNAs from 19 plant species showed that miRLocator performed better than the state-of-art miRNA predictor miRdup in locating mature miRNAs within plant pre-miRNAs. miRLocator will aid researchers interested in discovering miRNAs from model and non-model plant species.