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.

     

Origin of cells and network information

All cells are derived from one cell, and the origin of different cell types is a subject of curiosity. Cells construct life through appropriately timed networks at each stage of development. Communication among cells and intracellular signaling are essential for cell differentiation and for life processes. Cellular molecular networks establish cell diversity and life. The investigation of the regulation of each gene in the genome within the cellular network is therefore of interest. Stem cells produce various cells that are suitable for specific purposes. The dynamics of the information in the cellular network changes as the status of cells is altered. The components of each cell are subject to investigation.     

Development specifies,diversifies and empowers root immunity

Roots are a highly organised plant tissue consisting of different cell types with distinct developmental functions defined by cell identity networks. Roots are the target of some of the most devastating diseases and possess a highly effective immune system. The recognition of microbe‐ or plant‐derived molecules released in response to microbial attack is highly important in the activation of complex immunity gene networks. Development and immunity are intertwined, and immunity activation can result in growth inhibition. In turn, by connecting immunity and cell identity regulators, cell types are able to launch a cell type‐specific immunity based on the developmental function of each cell type. By this strategy, fundamental developmental processes of each cell type contribute their most basic functions to drive cost‐effective but highly diverse and, thus, efficient immune responses. This review highlights the interdependence of root development and immunity and how the developmental age of root cells contributes to positive and negative outcomes of development‐immunity cross‐talk.     

Selection for distinct gene expression properties favours the evolution of mutational robustness in gene regulatory networks

Mutational robustness is a genotype's tendency to keep a phenotypic trait with little and few changes in the face of mutations. Mutational robustness is both ubiquitous and evolutionarily important as it affects in different ways the probability that new phenotypic variation arises. Understanding the origins of robustness is specially relevant for systems of development that are phylogenetically widespread and that construct phenotypic traits with a strong impact on fitness. Gene regulatory networks are examples of this class of systems. They comprise sets of genes that, through cross‐regulation, build the gene activity patterns that define cellular responses, different tissues or distinct cell types. Several empirical observations, such as a greater robustness of wild‐type phenotypes, suggest that stabilizing selection underlies the evolution of mutational robustness. However, the role of selection in the evolution of robustness is still under debate. Computer simulations of the dynamics and evolution of gene regulatory networks have shown that selection for any gene activity pattern that is steady and self‐sustaining is sufficient to promote the evolution of mutational robustness. Here, I generalize this scenario using a computational model to show that selection for different aspects of a gene activity phenotype increases mutational robustness. Mutational robustness evolves even when selection favours properties that conflict with the stationarity of a gene activity pattern. The results that I present support an important role for stabilizing selection in the evolution of robustness in gene regulatory networks.     

Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer

Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these “silent players”. For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.     

Topology of gene expression networks as revealed by data mining and modeling

MOTIVATION: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data. In the present paper, we focus on several important issues related to the topology of gene expression networks that have not yet been fully studied. RESULTS: The networks derived from gene expression profiles for both time series experiments in yeast and perturbation experiments in cell lines are studied. We demonstrate that independent from the experimental organism (yeast versus cell lines) and the type of experiment (time courses versus perturbations) the extracted networks have similar topological characteristics suggesting together with the results of other common principles of the structural organization of biological networks. A novel computational model of network growth that reproduces the basic design principles of the observed networks is presented. Advantage of the model is that it provides a general mechanism to generate networks with different types of topology by a variation of a few parameters. We investigate the robustness of the network structure to random damages and to deliberate removal of the most important parts of the system and show a surprising tolerance of gene expression networks to both kinds of disturbance.     

整合受体调控基因表达信息构建细胞通信网络

目的 构建细胞通信网络有助于揭示细胞间协同工作机制、生物学过程和疾病发病机理。目前基于配体-受体相互作用构建细胞通信网络的方法大多只考虑配体和受体的表达信息，忽略了受体对其调控基因的信号传递影响，导致构建的细胞通信网络可靠性较低。鉴于此，本文提出IRRG算法，旨在构建更为准确的细胞通信网络，并挖掘具有生物学意义的细胞通信模式。方法 本文提出了一种整合受体调控基因表达信息构建细胞通信网络的方法（命名为IRRG）。该方法通过随机游走方式计算受体对下游基因的影响得分，进而与配体-受体共表达量结合构建细胞通信网络。结果 使用IRRG构建了小鼠滤泡间表皮（IFE）细胞通信网络并分析了配体-受体对的生物学意义，验证了IRRG计算受体影响得分的稳定性和细胞通信网络构建的可靠性。此外，使用IRRG构建了透明细胞肾细胞癌（ccRCC）的细胞通信网络，挖掘并分析其肿瘤微环境细胞通信模式。结论 IRRG可以构建富有生物学意义并且可靠的细胞通信网络，帮助人们从细胞通信的角度更深入地了解多种生物过程。IRRG算法代码可从GitHub获取：https://github.com/NWPU-903PR/IRRG。     

Inference of the Xenopus tropicalis embryonic regulatory network and spatial gene expression patterns

Background

During embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. One of the earliest events in the development of the vertebrate embryo is the establishment of three germ layers, consisting of the ectoderm, mesoderm and endoderm. Attempts to measure gene expression in vivo in different germ layers and cell types are typically complicated by the heterogeneity of cell types within biological samples (i.e., embryos), as the responses of individual cell types are intermingled into an aggregate observation of heterogeneous cell types. Here, we propose a novel method to elucidate gene regulatory circuits from these aggregate measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and then test the ability of the inferred networks to predict spatial gene expression patterns.

Results

We use two inference models with different underlying assumptions that incorporate existing network information, an ODE model for steady-state data and a Markov model for time series data, and contrast the performance of the two models. We apply our method to both control and knockdown embryos at multiple time points to reconstruct the core mesoderm and endoderm regulatory circuits. Those inferred networks are then used in combination with known dorsal-ventral spatial expression patterns of a subset of genes to predict spatial expression patterns for other genes. Both models are able to predict spatial expression patterns for some of the core mesoderm and endoderm genes, but interestingly of different gene subsets, suggesting that neither model is sufficient to recapitulate all of the spatial patterns, yet they are complementary for the patterns that they do capture.

Conclusion

The presented methodology of gene network inference combined with spatial pattern prediction provides an additional layer of validation to elucidate the regulatory circuits controlling the spatial-temporal dynamics in embryonic development.     

Genetic networks and soft computing

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.     

Efficient GFP‐labeling and analysis of spermatogenic cells using the IRG transgene and flow cytometry

Spermatogenesis is a highly ordered developmental program that produces haploid male germ cells. The study of male germ cell development in the mouse has provided unique perspectives into the molecular mechanisms that control cell development and differentiation in mammals, including tissue‐specific gene regulatory programs. An intrinsic challenge in spermatogenesis research is the heterogeneity of germ and somatic cell types present in the testis. Techniques to separate and isolate distinct mouse spermatogenic cell types have great potential to shed light on molecular mechanisms controlling mammalian cell development, while also providing new insights into cellular events important for human reproductive health. Here, we detail a versatile strategy that combines Cre‐lox technology to fluorescently label germ cells, with flow cytometry to discriminate and isolate germ cells in different stages of development for cellular and molecular analyses.     

Multi‐color FISH facilitates analysis of cell‐type diversification and developmental gene regulation in the Parasteatoda spider embryo

The simultaneous and quantitative analysis of the expression of multiple genes helps to shed light on gene regulatory networks. We established a method for multi‐color fluorescence in situ hybridization (mFISH) for the analysis of cell‐type diversification and developmental gene regulation in the embryo of the spider Parasteatoda tepidariorum. This mFISH technique allowed quadruple staining using four types of labels for RNA probes, digoxigenin, fluorescein, biotin, and dinitrophenyl, together with different fluorescent tyramides. To validate the usability of mFISH, we conducted four experiments. First, we distinguished similar gene expression patterns with mFISH, which showed overlaps and differences in the expression domains of anterior patterning hedgehog (hh), orthodenticle (otd), and labial genes at a cellular resolution. Second, we used mFISH to identify early cell types that are internalized on the anterior side. We found that fork head‐positive cells were subdivided into two cell types, 012_A08‐positive endoderm cells and twist‐positive mesoderm cells. Third, we quantified the ratio of expression levels of the odd‐paired (opa) gene in the chelicera and pedipalp segments based on the intensity of mFISH signals. Finally, we combined mFISH with embryonic RNA interference. It was possible to identify opa knockdown cell clones and detect the specific reduction of opa and the upregulation of otd and hh expression levels in the same cell clone that formed in the head region. This study proposes that mFISH is a powerful tool for the cell‐level analysis of gene regulation and quantification in the spider model.     

Gene networks in development

A model is presented for the formation of temporal and spatial patterns of cell types during the development of organisms. It is demonstrated that very simple random networks of interactions among genes that affect expression may lead to the autonomous development of patterns of cell types. It is required that the networks contain active feedback loops and that there is limited communication among cells. The only elements of the model, gene interactions, are specified by the DNA nucleotide sequences of the genes. Therefore, the model readily explains how the control of development is specified by the organism's DNA. In the context of this model, the formation of positional information and its interpretation becomes a single process.     

Digital Single-Cell Analysis of Plant Organ Development Using 3DCellAtlas

Diverse molecular networks underlying plant growth and development are rapidly being uncovered. Integrating these data into the spatial and temporal context of dynamic organ growth remains a technical challenge. We developed 3DCellAtlas, an integrative computational pipeline that semiautomatically identifies cell types and quantifies both 3D cellular anisotropy and reporter abundance at single-cell resolution across whole plant organs. Cell identification is no less than 97.8% accurate and does not require transgenic lineage markers or reference atlases. Cell positions within organs are defined using an internal indexing system generating cellular level organ atlases where data from multiple samples can be integrated. Using this approach, we quantified the organ-wide cell-type-specific 3D cellular anisotropy driving Arabidopsis thaliana hypocotyl elongation. The impact ethylene has on hypocotyl 3D cell anisotropy identified the preferential growth of endodermis in response to this hormone. The spatiotemporal dynamics of the endogenous DELLA protein RGA, expansin gene EXPA3, and cell expansion was quantified within distinct cell types of Arabidopsis roots. A significant regulatory relationship between RGA, EXPA3, and growth was present in the epidermis and endodermis. The use of single-cell analyses of plant development enables the dynamics of diverse regulatory networks to be integrated with 3D organ growth.