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.

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.

scholarly journals A genome-wide transcriptional analysis of morphology determination inCandida albicans

2013 ◽  
Vol 24 (3) ◽  
pp. 246-260 ◽  
Author(s):  
Patricia L. Carlisle ◽  
David Kadosh
Keyword(s):  
Gene Expression ◽  
Fungal Infections ◽  
Transcriptional Profiling ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Transcriptional Analysis ◽  
Specific Gene ◽  
Genome Wide ◽  
A Genome ◽  
Genetically Manipulated

Candida albicans, the most common cause of human fungal infections, undergoes a reversible morphological transition from yeast to pseudohyphal and hyphal filaments, which is required for virulence. For many years, the relationship among global gene expression patterns associated with determination of specific C. albicans morphologies has remained obscure. Using a strain that can be genetically manipulated to sequentially transition from yeast to pseudohyphae to hyphae in the absence of complex environmental cues and upstream signaling pathways, we demonstrate by whole-genome transcriptional profiling that genes associated with pseudohyphae represent a subset of those associated with hyphae and are generally expressed at lower levels. Our results also strongly suggest that in addition to dosage, extended duration of filament-specific gene expression is sufficient to drive the C. albicans yeast-pseudohyphal-hyphal transition. Finally, we describe the first transcriptional profile of the C. albicans reverse hyphal-pseudohyphal-yeast transition and demonstrate that this transition involves not only down-regulation of known hyphal-specific, genes but also differential expression of additional genes that have not previously been associated with the forward transition, including many involved in protein synthesis. These findings provide new insight into genome-wide expression patterns important for determining fungal morphology and suggest that in addition to similarities, there are also fundamental differences in global gene expression as pathogenic filamentous fungi undergo forward and reverse morphological transitions.

scholarly journals Comparative analyses of saprotrophy in Salisapilia sapeloensis and diverse plant pathogenic oomycetes reveal lifestyle-specific gene expression

2020 ◽  
Vol 96 (11) ◽  
Author(s):  
Sophie de Vries ◽  
Jan de Vries ◽  
John M Archibald ◽  
Claudio H Slamovits
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Plant Pathogens ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Reference Points ◽  
Global Perspective ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Specific Array

ABSTRACT Oomycetes include many devastating plant pathogens. Across oomycete diversity, plant-infecting lineages are interspersed by non-pathogenic ones. Unfortunately, our understanding of the evolution of lifestyle switches is hampered by a scarcity of data on the molecular biology of saprotrophic oomycetes, ecologically important primary colonizers of dead tissue that can serve as informative reference points for understanding the evolution of pathogens. Here, we established Salisapilia sapeloensis as a tractable system for the study of saprotrophic oomycetes. We generated multiple transcriptomes from S. sapeloensis and compared them with (i) 22 oomycete genomes and (ii) the transcriptomes of eight pathogenic oomycetes grown under 13 conditions. We obtained a global perspective on gene expression signatures of oomycete lifestyles. Our data reveal that oomycete saprotrophs and pathogens use similar molecular mechanisms for colonization but exhibit distinct expression patterns. We identify a S. sapeloensis-specific array and expression of carbohydrate-active enzymes and putative regulatory differences, highlighted by distinct expression levels of transcription factors. Salisapilia sapeloensis expresses only a small repertoire of candidates for virulence-associated genes. Our analyses suggest lifestyle-specific gene regulatory signatures and that, in addition to variation in gene content, shifts in gene regulatory networks underpin the evolution of oomycete lifestyles.

scholarly journals Analysis of Spatio-Temporal Transcriptome Profiles of Soybean (Glycine max) Tissues during Early Seed Development

2020 ◽  
Vol 21 (20) ◽  
pp. 7603
Author(s):  
Shuo Sun ◽  
Changyu Yi ◽  
Jing Ma ◽  
Shoudong Wang ◽  
Marta Peirats-Llobet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Glycine Max ◽  
Seed Development ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Soybean Seed ◽  
Endosperm Development ◽  
A Genome ◽  
Early Seed Development ◽  
Spatio Temporal

Soybean (Glycine max) is an important crop providing oil and protein for both human and animal consumption. Knowing which biological processes take place in specific tissues in a temporal manner will enable directed breeding or synthetic approaches to improve seed quantity and quality. We analyzed a genome-wide transcriptome dataset from embryo, endosperm, endothelium, epidermis, hilum, outer and inner integument and suspensor at the global, heart and cotyledon stages of soybean seed development. The tissue specificity of gene expression was greater than stage specificity, and only three genes were differentially expressed in all seed tissues. Tissues had both unique and shared enriched functional categories of tissue-specifically expressed genes associated with them. Strong spatio-temporal correlation in gene expression was identified using weighted gene co-expression network analysis, with the most co-expression occurring in one seed tissue. Transcription factors with distinct spatiotemporal gene expression programs in each seed tissue were identified as candidate regulators of expression within those tissues. Gene ontology (GO) enrichment of orthogroup clusters revealed the conserved functions and unique roles of orthogroups with similar and contrasting expression patterns in transcript abundance between soybean and Arabidopsis during embryo proper and endosperm development. Key regulators in each seed tissue and hub genes connecting those networks were characterized by constructing gene regulatory networks. Our findings provide an important resource for describing the structure and function of individual soybean seed compartments during early seed development.

scholarly journals Comparative analyses of saprotrophy in Salisapilia sapeloensis and diverse plant pathogenic oomycetes reveal lifestyle-specific gene expression

2019 ◽  
Author(s):  
Sophie de Vries ◽  
Jan de Vries ◽  
John M Archibald ◽  
Claudio H Slamovits
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Plant Pathogens ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Reference Points ◽  
Global Perspective ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Specific Array

Oomycetes include many well-studied, devastating plant pathogens. Across oomycete diversity, plant-infecting lineages are interspersed by non-pathogenic ones. Unfortunately, our understanding of the evolution of lifestyle switches is hampered by a scarcity of data on the molecular biology of saprotrophic oomycetes, ecologically important primary colonizers of dead tissue that can serve as informative reference points for understanding the evolution of pathogens. Here, we established Salisapilia sapeloensis growing on axenic litter as a tractable system for the study of saprotrophic oomycetes. We generated multiple transcriptomes from S. sapeloensis and compared them to (a) 22 oomycete genomes and (b) the transcriptomes of eight pathogenic oomycetes grown under 13 conditions (three pathogenic lifestyles, six hosts/substrates, and four tissues). From these analyses we obtained a global perspective on the gene expression signatures of oomycete lifestyles. Our data reveal that oomycete saprotrophs and pathogens use generally similar molecular mechanisms for colonization, but exhibit distinct expression patterns. We identify S. sapeloensis' specific array and expression of carbohydrate-active enzymes and regulatory differences in pathogenicity-associated factors, including the virulence factor EpiC2B. Further, S. sapeloensis was found to express only a small repertoire of effector genes. In conclusion, our analyses reveal lifestyle-specific gene regulatory signatures and suggest that, in addition to variation in gene content, shifts in gene regulatory networks might underpin the evolution of oomycete lifestyles.

Gene expression–based classification and regulatory networks of pediatric acute lymphoblastic leukemia

Blood ◽  
2009 ◽  
Vol 114 (20) ◽  
pp. 4486-4493 ◽  
Author(s):  
Zhigang Li ◽  
Wei Zhang ◽  
Minyuan Wu ◽  
Shanshan Zhu ◽  
Chao Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Patient Group ◽  
Regulatory Networks ◽  
Lymphoblastic Leukemia ◽  
Expression Patterns ◽  
Independent Set ◽  
Specific Gene ◽  
Pediatric Acute Lymphoblastic Leukemia ◽  
Disease Subtype

Abstract Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining 3 published microarray datasets on 535 mostly white children's samples and generating a new dataset on 100 Chinese children's ALL samples, we were able to (1) identify a 62-gene classifier with 97.6% accuracy from the white children's samples and validated it on the completely independent set of 100 Chinese samples, and (2) uncover potential regulatory networks of ALL subtypes. The classifier we identified was, thus far, the only one that could be applied directly to a single sample and that sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL.

scholarly journals Sequence variation, common tissue expression patterns and learning models: a genome-wide survey of vertebrate ribosomal proteins

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Konstantinos A Kyritsis ◽  
Christos A Ouzounis ◽  
Lefteris Angelis ◽  
Ioannis S Vizirianakis
Keyword(s):  
Gene Expression ◽  
Ribosomal Proteins ◽  
Genetic Diseases ◽  
Expression Patterns ◽  
Ribosomal Genes ◽  
Specific Gene ◽  
Learning Models ◽  
Vertebrate Lineage ◽  
Sequence Comparisons ◽  
A Genome

Abstract Ribosomal genes produce the constituents of the ribosome, one of the most conserved subcellular structures of all cells, from bacteria to eukaryotes, including animals. There are notions that some protein-coding ribosomal genes vary in their roles across species, particularly vertebrates, through the involvement of some in a number of genetic diseases. Based on extensive sequence comparisons and systematic curation, we establish a reference set for ribosomal proteins (RPs) in eleven vertebrate species and quantify their sequence conservation levels. Moreover, we correlate their coordinated gene expression patterns within up to 33 tissues and assess the exceptional role of paralogs in tissue specificity. Importantly, our analysis supported by the development and use of machine learning models strongly proposes that the variation in the observed tissue-specific gene expression of RPs is rather species-related and not due to tissue-based evolutionary processes. The data obtained suggest that RPs exhibit a complex relationship between their structure and function that broadly maintains a consistent expression landscape across tissues, while most of the variation arises from species idiosyncrasies. The latter may be due to evolutionary change and adaptation, rather than functional constraints at the tissue level throughout the vertebrate lineage.

scholarly journals Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

2016 ◽  
Vol 113 (16) ◽  
pp. 4290-4295 ◽  
Author(s):  
Siqi Wu ◽  
Antony Joseph ◽  
Ann S. Hammonds ◽  
Susan E. Celniker ◽  
Bin Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Matrix Factorization ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Nonnegative Matrix Factorization ◽  
Expression Patterns ◽  
Selection Criterion ◽  
Nonnegative Matrix ◽  
Biological Information ◽  
Expression Data

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior–posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

scholarly journals Network analysis allows to unravel breast cancer molecular features and to identify novel targets

2019 ◽  
Author(s):  
Aurora Savino ◽  
Lidia Avalle ◽  
Emanuele Monteleone ◽  
Irene Miglio ◽  
Alberto Griffa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Biology ◽  
Gene Networks ◽  
High Reliability ◽  
Expression Patterns ◽  
Transcriptional Networks ◽  
Specific Gene ◽  
Breast Cancer Dataset ◽  
Molecular Features

AbstractThe behaviour of complex biological systems is determined by the orchestrated activity of many components interacting with each other, and can be investigated by networks. In particular, gene co-expression networks have been widely used in the past years thanks to the increasing availability of huge gene expression databases. Breast cancer is a heterogeneous disease usually classified either according to immunohistochemical features or by expression profiling, which identifies the 5 subtypes luminal A, luminal B, basal-like, HER2-positive and normal-like. Basal-like tumours are the most aggressive subtype, for which so far no targeted therapy is available.Making use of the WGCNA clustering method to reconstruct breast cancer transcriptional networks from the METABRIC breast cancer dataset, we developed a platform to address specific questions related to breast cancer biology. In particular, we obtained gene modules significantly correlated with survival and age of onset, useful to understand how molecular features and gene expression patterns are organized in breast cancer. We next generated subtype-specific gene networks and in particular identified two modules that are significantly more connected in basal-like breast cancer with respect to all other subtypes, suggesting relevant biological functions. We demonstrate that network centrality (kWithin) is a suitable measure to identify relevant genes, since we could show that it correlates with clinical features and that it provides a mean to select potential upstream regulators of a module with high reliability. Finally, we showed the feasibility of adding meaning to the networks by combining them with independently obtained data related to activated pathways.In conclusion, our platform allows to identify groups of genes highly relevant in breast cancer and possibly amenable to drug targeting, due to their ability to regulate survival-related gene networks. This approach could be successfully extended to other BC subtypes, and to all tumor types for which enough expression data are available.

A Global Vista of the Epigenomic State of the Mouse Submandibular Gland

2021 ◽  
pp. 002203452110120
Author(s):  
C. Gluck ◽  
S. Min ◽  
A. Oyelakin ◽  
M. Che ◽  
E. Horeth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcriptional Control ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Submandibular Salivary Gland ◽  
Data Sets ◽  
Control Mechanisms ◽  
Chromatin Immunoprecipitation Sequencing

The parotid, submandibular, and sublingual glands represent a trio of oral secretory glands whose primary function is to produce saliva, facilitate digestion of food, provide protection against microbes, and maintain oral health. While recent studies have begun to shed light on the global gene expression patterns and profiles of salivary glands, particularly those of mice, relatively little is known about the location and identity of transcriptional control elements. Here we have established the epigenomic landscape of the mouse submandibular salivary gland (SMG) by performing chromatin immunoprecipitation sequencing experiments for 4 key histone marks. Our analysis of the comprehensive SMG data sets and comparisons with those from other adult organs have identified critical enhancers and super-enhancers of the mouse SMG. By further integrating these findings with complementary RNA-sequencing based gene expression data, we have unearthed a number of molecular regulators such as members of the Fox family of transcription factors that are enriched and likely to be functionally relevant for SMG biology. Overall, our studies provide a powerful atlas of cis-regulatory elements that can be leveraged for better understanding the transcriptional control mechanisms of the mouse SMG, discovery of novel genetic switches, and modulating tissue-specific gene expression in a targeted fashion.

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