scholarly journals Fine-tuned adaptation of embryo-endometrium pairs at implantation revealed by gene regulatory networks Tailored conceptus-maternal communication at implantation

2018 ◽  
Author(s):  
Fernando H. Biase ◽  
Isabelle Hue ◽  
Sarah E. Dickinson ◽  
Florence Jaffrezic ◽  
Denis Laloe ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Strong Dependence ◽  
Expression Patterns ◽  
Transcript Abundance ◽  
Response Characteristic ◽  
Regulation Of Transcription ◽  
Functional Modules ◽  
Functional Analyses ◽  
Extraembryonic Tissues

ABSTRACTInteractions between embryo and endometrium at implantation are critical for the progression and the issue of pregnancy. These reciprocal actions involve exchange of paracrine signals that govern implantation and placentation. However, it remains unknown how these interactions between the conceptus and the endometrium are coordinated at the level of an individual pregnancy. Under the hypothesis that gene expression of endometrium is dependent on gene expression of extraembryonic tissues, we performed an integrative analysis of transcriptome profiles of paired conceptuses and endometria obtained from pregnancies initiated by artificial insemination. We quantified strong dependence (|r|>0.95, eFDR<0.01) in transcript abundance of genes expressed in the extraembryonic tissues and genes expressed in the endometrium. The profiles of connectivity revealed distinct co-expression patterns of extraembryonic tissues with caruncular and intercaruncular areas of the endometrium. Notably, a subset of highly co-expressed genes between conceptus (n=229) and caruncular areas of the endometrium (n=218, r>0.9999, eFDR<0.001) revealed a blueprint of gene expression specific to each pregnancy. Functional analyses of genes co-expressed between conceptus and endometrium revealed significantly enriched functional modules with critical contribution for implantation and placentation, including “in utero embryonic development”, “placenta development” and “regulation of transcription”. Functional modules were remarkably specific to caruncular or intercaruncular areas of the endometrium. The quantitative and functional association between genes expressed in conceptus and endometrium emphasize a coordinated communication between these two entities in mammals. To our knowledge, we provide first evidence that implantation in mammalian pregnancy relies on the ability of the conceptus and the endometrium to develop a fine-tuned adaptive response characteristic of each pregnancy.

scholarly journals Joint profiling of gene expression and chromatin accessibility of amphioxus development at single cell resolution

2021 ◽  
Author(s):  
Pengcheng Ma ◽  
Xingyan Liu ◽  
Huimin Liu ◽  
Zaoxu Xu ◽  
Xiangning Ding ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Functional Divergence ◽  
Expression Patterns ◽  
Genetic Regulatory Networks ◽  
Public Access ◽  
Origin And Evolution ◽  
Key Species

Abstract Vertebrate evolution was accompanied with two rounds of whole genome duplication followed by functional divergence in terms of regulatory circuits and gene expression patterns. As a basal and slow-evolving chordate species, amphioxus is an ideal paradigm for exploring the origin and evolution of vertebrates. Single cell sequencing has been widely employed to construct the developmental cell atlas of several key species of vertebrates (human, mouse, zebrafish and frog) and tunicate (sea squirts). Here, we performed single-nucleus RNA sequencing (snRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) for different stages of amphioxus (covering embryogenesis and adult tissues). With the datasets generated we constructed the developmental tree for amphioxus cell fate commitment and lineage specification, and revealed the underlying key regulators and genetic regulatory networks. The generated data were integrated into an online platform, AmphioxusAtlas, for public access at http://120.79.46.200:81/AmphioxusAtlas.

scholarly journals Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

2020 ◽  
Author(s):  
Alexander Calderwood ◽  
Jo Hepworth ◽  
Shannon Woodhouse ◽  
Lorelei Bilham ◽  
D. Marc Jones ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Detailed Comparison ◽  
Developmental Time ◽  
Floral Transition ◽  
Gene Regulatory

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

scholarly journals Global Histone H3 Lysine 4 Trimethylation (H3K4me3) Landscape Changes in Response to TGFβ

2021 ◽  
Vol 14 ◽  
pp. 251686572110517
Author(s):  
Ankit Naik ◽  
Nidhi Dalpatraj ◽  
Noopur Thakur
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Expression Patterns ◽  
Histone H3 ◽  
Epigenetic Mechanism ◽  
Stable Gene ◽  
Regulation Of Transcription ◽  
Functional Categories ◽  
Positive Regulation ◽  
Histone H4 Acetylation

TGFβ expression acts as a biomarker of poor prognosis in prostate cancer. It plays a dual functional role in prostate cancer. In the early stages of the tumor, it acts as a tumor suppressor while at the later stages of tumor development, it promotes metastasis. The molecular mechanisms of action of TGFβ are largely understood through the canonical and non-canonical signal transduction pathways. Our understanding of the mechanisms that establish transient TGFβ stimulation into stable gene expression patterns remains incomplete. Epigenetic marks like histone H3 modifications are directly linked with gene expression and they play an important role in tumorigenesis. In this report, we performed chromatin immunoprecipitation-sequencing (ChIP-Seq) to identify the genome-wide regions that undergo changes in histone H3 Lysine 4 trimethylation (H3K4me3) occupancy in response to TGFβ stimulation. We also show that TGFβ stimulation can induce acute epigenetic changes through the modulation of H3K4me3 signals at genes belonging to special functional categories in prostate cancer. TGFβ induces the H3K4me3 on its own ligands like TGFβ, GDF1, INHBB, GDF3, GDF6, BMP5 suggesting a positive feedback loop. The majority of genes were found to be involved in the positive regulation of transcription from the RNA polymerase II promoter in response to TGFβ. Other functional categories were intracellular protein transport, brain development, EMT, angiogenesis, antigen processing, antigen presentation via MHC class II, lipid transport, embryo development, histone H4 acetylation, positive regulation of cell cycle arrest, and genes involved in mitotic G2 DNA damage checkpoints. Our results link TGFβ stimulation to acute changes in gene expression through an epigenetic mechanism. These findings have broader implications on epigenetic bases of acute gene expression changes caused by growth factor stimulation.

scholarly journals Synthetic gene regulatory networks in the opportunistic human pathogen Streptococcus pneumoniae

2019 ◽  
Author(s):  
Robin A. Sorg ◽  
Clement Gallay ◽  
Jan-Willem Veening
Keyword(s):  
Gene Expression ◽  
Streptococcus Pneumoniae ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Combinatorial Libraries ◽  
Regulatory Elements ◽  
Expression Control ◽  
Gene Expression Control

AbstractStreptococcus pneumoniae can cause disease in various human tissues and organs, including the ear, the brain, the blood and the lung, and thus in highly diverse and dynamic environments. It is challenging to study how pneumococci control virulence factor expression, because cues of natural environments and the presence of an immune system are difficult to simulate in vitro. Here, we apply synthetic biology methods to reverse-engineer gene expression control in S. pneumoniae. A selection platform is described that allows for straightforward identification of transcriptional regulatory elements out of combinatorial libraries. We present TetR- and LacI-regulated promoters that show expression ranges of four orders of magnitude. Based on these promoters, regulatory networks of higher complexity are assembled, such as logic AND and IMPLY gates. Finally, we demonstrate single-copy genome-integrated toggle switches that give rise to bimodal population distributions. The tools described here can be used to mimic complex expression patterns, such as the ones found for pneumococcal virulence factors, paving the way for in vivo investigations of the importance of gene expression control on the pathogenicity of S. pneumoniae.

scholarly journals Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stuart P. Wilson ◽  
Sebastian S. James ◽  
Daniel J. Whiteley ◽  
Leah A. Krubitzer
Keyword(s):  
Gene Expression ◽  
Limit Cycles ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Specific Gene ◽  
Boolean Models ◽  
A Genome ◽  
Order Of Magnitude ◽  
Accelerate Evolution

AbstractDevelopmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

ON CONSTRUCTION OF STOCHASTIC GENETIC NETWORKS BASED ON GENE EXPRESSION SEQUENCES

2005 ◽  
Vol 15 (04) ◽  
pp. 297-310 ◽  
Author(s):  
WAI-KI CHING ◽  
MICHAEL M. NG ◽  
ERIC S. FUNG ◽  
TATSUYA AKUTSU
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Boolean Networks ◽  
Genetic Regulatory Networks ◽  
Efficient Estimation ◽  
Estimation Methods ◽  
Series Data ◽  
Model Parameters ◽  
Proposed Model

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

scholarly journals Expression pattern determines regulatory logic

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244864
Author(s):  
Carlos Mora-Martinez
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Dna Sequences ◽  
In Silico ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Types ◽  
Evolutionary Strategy ◽  
Specific Gene ◽  
Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

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