Expression Dynamics of Genes and microRNAs at Different Growth Stages and Heat Treatments in Contrasting High Temperature Responsive Rice Genotypes

The global warming-driven climate change is becoming a major challenge for rice cultivation in Asia and Africa. High-temperature stress impairs the physiology and growth of rice plant, and ultimately results in reduced grain yield. This study was aimed to decipher the physiological and molecular changes occurring during different growth stages of heat-tolerant (N22) and -susceptible (Vandana) rice cultivars under three different heat treatments. Chlorophyll content, membrane integrity, gas exchange parameters and expression of genes and miRNAs were analyzed in N22 and Vandana at seedling, vegetative, and reproductive growth stages after exposing to short and long duration of high temperature stress, and recovery. A number of genes and miRNAs showed dynamic changes in their expression patterns at different growth stages and heat treatments, highlighting the necessity to understand gene regulation before employing the genes for modification through transgenic or gene editing approaches. Predominantly N22 showed distinct and unique capability to reprogram its physiological and molecular machinery during prolonged heat stress at reproductive stage, suggesting that the dynamics in gene regulation is crucial to determine its heat tolerant ability. The study has larger implications in deploying genes for the development of heat tolerant rice cultivars through breeding, transgenic, and genome editing approaches.

     

Genome-wide analysis of epigenetic dynamics across human developmental stages and tissues

Background

Epigenome is highly dynamic during the early stages of embryonic development. Epigenetic modifications provide the necessary regulation for lineage specification and enable the maintenance of cellular identity. Given the rapid accumulation of genome-wide epigenomic modification maps across cellular differentiation process, there is an urgent need to characterize epigenetic dynamics and reveal their impacts on differential gene regulation.

Methods

We proposed DiffEM, a computational method for differential analysis of epigenetic modifications and identified highly dynamic modification sites along cellular differentiation process. We applied this approach to investigating 6 epigenetic marks of 20 kinds of human early developmental stages and tissues, including hESCs, 4 hESC-derived lineages and 15 human primary tissues.

Results

We identified highly dynamic modification sites where different cell types exhibit distinctive modification patterns, and found that these highly dynamic sites enriched in the genes related to cellular development and differentiation. Further, to evaluate the effectiveness of our method, we correlated the dynamics scores of epigenetic modifications with the variance of gene expression, and compared the results of our method with those of the existing algorithms. The comparison results demonstrate the power of our method in evaluating the epigenetic dynamics and identifying highly dynamic regions along cell differentiation process.

     

Designing hybrid life cycle assessment models based on uncertainty and complexity

Purpose

Despite the wide use of LCA for environmental profiling, the approach for determining the system boundary within LCA models continues to be subjective and lacking in mathematical rigor. As a result, life cycle models are often developed in an ad hoc manner, and are difficult to compare. Significant environmental impacts may be inadvertently left out. Overcoming this shortcoming can help elicit greater confidence in life cycle models and their use for decision making.

Methods

This paper describes a framework for hybrid life cycle model generation by selecting activities based on their importance, parametric uncertainty, and contribution to network complexity. The importance of activities is determined by structural path analysis—which then guides the construction of life cycle models based on uncertainty and complexity indicators. Information about uncertainty is from the available life cycle inventory; complexity is quantified by cost or granularity. The life cycle model is developed in a hierarchical manner by adding the most important activities until error requirements are satisfied or network complexity exceeds user-specified constraints.

Results and Discussion

The framework is applied to an illustrative example for building a hybrid LCA model. Since this is a constructed example, the results can be compared with the actual impact, to validate the approach. This application demonstrates how the algorithm sequentially develops a life cycle model of acceptable uncertainty and network complexity. Challenges in applying this framework to practical problems are discussed.

Conclusion

The presented algorithm designs system boundaries between scales of hybrid LCA models, includes or omits activities from the system based on path analysis of environmental impact contribution at upstream network nodes, and provides model quality indicators that permit comparison between different LCA models.

     

Kinetic Mechanisms of Biological Regulation in Photosynthetic Organisms

Principles of regulation on different levels of photosynthetic apparatus are discussed. Mathematical models of isolated photosynthetic reaction centers and general system of energy transduction in chloroplast are developed. A general approach to model these complex metabolic systems is suggested. Regulatory mechanisms in plant cell are correlated with the different patterns of fluorescence induction curve at different internal physiological states of the cells and external (environmental) conditions. Light regulation inside photosynthetic reaction centers, diffusion processes in thylakoid membrane, generation of transmembrane electrochemical potential, coupling with processes of CO₂ fixation in Calvin Cycle are considered as stages of control of energy transformation in chloroplasts in their connection with kinetic patterns of fluorescence induction curves and other spectrophotometric data.     

From blastocyst to gastrula: gene regulatory networks of embryonic stem cells and early mouse embryogenesis

To date, many regulatory genes and signalling events coordinating mammalian development from blastocyst to gastrulation stages have been identified by mutational analyses and reverse-genetic approaches, typically on a gene-by-gene basis. More recent studies have applied bioinformatic approaches to generate regulatory network models of gene interactions on a genome-wide scale. Such models have provided insights into the gene networks regulating pluripotency in embryonic and epiblast stem cells, as well as cell-lineage determination in vivo. Here, we review how regulatory networks constructed for different stem cell types relate to corresponding networks in vivo and provide insights into understanding the molecular regulation of the blastocyst–gastrula transition.     

Validation and refinement of gene-regulatory pathways on a network of physical interactions

As genome-scale measurements lead to increasingly complex models of gene regulation, systematic approaches are needed to validate and refine these models. Towards this goal, we describe an automated procedure for prioritizing genetic perturbations in order to discriminate optimally between alternative models of a gene-regulatory network. Using this procedure, we evaluate 38 candidate regulatory networks in yeast and perform four high-priority gene knockout experiments. The refined networks support previously unknown regulatory mechanisms downstream of SOK2 and SWI4.     

Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.     

A cellular automaton model of morphogenesis in Arabidopsis thaliana

Development of organisms is a very complex process in that a lot of gene networks of different cell types are to be integrated. Development of cellular automata that model the morphodynamics of different cell types is the first step in understanding and analyzing the regulatory mechanisms that underlie the developmental gene networks. We have developed a model of a cellular automaton that simulates the embryonic development of the shoot meristem in Arabidopsis thaliana. The model adequately describes the basic stages in the development of this organ in wild type and mutants.

     

基于双聚类挖掘癌症共享的基因功能模块

基因多效性是癌症遗传机制中的普遍现象, 但罕见系统性的分析。文章提出采用双聚类挖掘基因功能模块的新思路探索癌症的共享分子机制和不同癌症间的关系。获取20种癌症的基因表达数据, 应用改良t检验和倍数法筛选出至少在两种癌症中差异表达的基因, 得到10417×20的数据矩阵; 采用双聚类方法获得22个癌症共享的基因簇; 进一步富集分析得到17个基因功能模块(Bonferroni校正后P<0.05), 主要参与有丝分裂染色单体分离的调控、细胞分化、免疫和炎症反应、胶原纤维组织等生物过程; 主要执行ATP结合和微管活动、MHCⅡ类受体活性、肽链内切酶抑制活性等分子功能; 活动区域主要在细胞骨架、染色体、MHCⅡ蛋白质复合体、中间丝蛋白、胶原纤维等。基于模块构建癌症相关网络, 显示胃癌、卵巢腺癌、宫颈鳞癌和间皮瘤等之间相关程度较高, 而两种血液系统癌症(急性髓细胞性白血病与多发性骨髓瘤)分子机制与其他癌症存在较大差异。可见癌症共享的基因功能模块与多种生物机制有关, 癌症之间相似性可能与组织起源、共同的致癌机制等有关。文章提出的基因多效性分析方法有助于解释人类复杂性疾病的共享分子机制。