scholarly journals MCO: towards an ontology and unified vocabulary for a framework-based annotation of microbial growth conditions

2017 ◽  
Author(s):  
VH Tierrafría ◽  
C Mejía-Almonte ◽  
JM Camacho-Zaragoza ◽  
H Salgado ◽  
K Alquicira ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Phenotypic Expression ◽  
Biomedical Ontology ◽  
Growth Conditions ◽  
Controlled Vocabulary ◽  
Experimental Conditions ◽  
E Coli ◽  
K 12

AbstractMotivationA major component in our understanding of the biology of an organism is the mapping of its genotypic potential into the repertoire of its phenotypic expression profiles. This genotypic to phenotypic mapping is executed by the machinery of gene regulation that turns genes on and off, which in microorganisms is essentially studied by changes in growth conditions and genetic modifications. Although many efforts have been made to systematize the annotation of experimental conditions in microbiology, the available annotation is not based on a consistent and controlled vocabulary for the unambiguous description of growth conditions, making difficult the identification of biologically meaningful comparisons of knowledge generated in different experiments or laboratories, a task urgently needed given the massive amounts of data generated by high throughput (HT) technologies.ResultsWe curated terms related to experimental conditions that affect gene expression inE. coliK-12. Since this is the best studied microorganism, the collected terms are the seed for the first version of the Microbial Conditions Ontology (MCO), a controlled and structured vocabulary that can be expanded to annotate microbial conditions in general. Moreover, we developed an annotation framework using the MCO terms to describe experimental conditions, providing the foundation to identify regulatory networks that operate under a particular condition. MCO supports comparisons of HT-derived data from different repositories. In this sense, we started to map common RegulonDB terms and Colombos bacterial expression compendia terms to MCO.Availability and ImplementationAs far as we know, MCO is the first ontology for growth conditions of any bacterial organism and it is available athttp://regulondb.ccg.unam.mx/. Furthermore, we will disseminate MCO throughout the Open Biomedical Ontology (OBO) Foundry in order to set a standard for the annotation of gene expression data derived from conventional as well as HT experiments inE. coliand other microbial organisms. This will enable the comparison of data from diverse data [email protected],[email protected]

scholarly journals Towards a unified resource for transcriptional regulation inEscherichia coliK-12: Incorporating high throughput-generated binding data within the classic framework of regulation of initiation of transcription in RegulonDB

2017 ◽  
Author(s):  
Alberto Santos-Zavaleta ◽  
Mishael Sánchez-Pérez ◽  
Heladia Salgado ◽  
David A. Velázquez-Ramírez ◽  
Socorro Gama-Castro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Molecular Biology ◽  
High Throughput ◽  
Expression Profiles ◽  
Regulation Of Gene Expression ◽  
Controlled Vocabulary ◽  
Data Set ◽  
E Coli ◽  
K 12

ABSTRACTOur understanding of the regulation of gene expression has been strongly benefited by the availability of high throughput technologies that enable questioning the whole genome for the binding of specific transcription factors and expression profiles. In the case of genome models, such asEscherichia coliK-12, this knowledge needs to be integrated with the legacy of accumulated genetics and molecular biology pre-genomic knowledge in order to attain deeper levels in the understanding of their biology. In spite of the several repositories and curated databases, there is no effort, nor electronic site yet, to comprehensively integrate the available knowledge from all these different sources around the regulation of gene expression ofE. coliK-12. In this paper, we describe a first effort to expand RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization inE. coliK-12, to include the genome-wide data set collections from 25 ChIP and 18 gSELEX publications, respectively, in addition to around 60 expression profiles used in their curation. Three essential features for the integration of this information coming from different methodological approaches are; first, a controlled vocabulary within an ontology for precisely defining growth conditions, second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated sites, and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration strongly needed to manage and access such wealth of knowledge. This version of RegulonBD is a first step toward what should become the unifying access point for current and future knowledge on gene regulation inE. coliK-12. Furthermore, this model platform and associated methodologies and criteria, can well be emulated for gathering knowledge on other microbial organisms.

scholarly journals EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

2021 ◽  
Author(s):  
Hakimeh Khojasteh ◽  
Mohammad Hossein Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Data ◽  
Class Imbalance Problem ◽  
Experimental Conditions ◽  
Topological Features ◽  
Gene Regulatory

The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a challenging task. In this paper, we propose EnGRNT approach to infer GRNs with high accuracy that uses ensemble-based methods. The proposed approach, as well as the gene expression data, considers the topological features of GRN. We applied our approach to the simulated Escherichia coli dataset. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. The obtained results recommend the application of EnGRNT on the imbalanced datasets.

scholarly journals Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae

2008 ◽  
Vol 2 ◽  
pp. BBI.S853 ◽  
Author(s):  
Jesper Lundström ◽  
Johan Björkegren ◽  
Jesper Tegnér
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Repeated Measurements ◽  
Experimental Conditions ◽  
Cellular Processes ◽  
Binding Data ◽  
External Stimuli

Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.

scholarly journals PSIV-11 Effects of very low-dose antibiotics on gene expression profiles in ileal mucosa of weaned pigs infected with a pathogenic E. coli

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 286-286
Author(s):  
Kwangwook Kim ◽  
Sungbong Jang ◽  
Yanhong Liu
Keyword(s):  
Gene Expression ◽  
Systemic Inflammation ◽  
Low Dose ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Post Inoculation ◽  
Growth Promoter ◽  
Weaned Pigs ◽  
Ileal Mucosa ◽  
E Coli

Abstract Our previous studies have shown that supplementation of low-dose antibiotic growth promoter (AGP) exacerbated growth performance and systemic inflammation of weaned pigs infected with pathogenic Escherichia coli (E. coli). The objective of this experiment, which is extension of our previous report, was to investigate the effect of low-dose AGP on gene expression in ileal mucosa of weaned pigs experimentally infected with F18 E. coli. Thirty-four pigs (6.88 ± 1.03 kg BW) were individually housed in disease containment rooms and randomly allotted to one of three treatments (9 to 13 pigs/treatment). The three dietary treatments were control diet (control), and 2 additional diets supplemented with 0.5 or 50 mg/kg of AGP (carbadox), respectively. The experiment lasted 18 d [7 d before and 11 d after first inoculation (d 0)]. The F18 E. coli inoculum was orally provided to all pigs with the dose of 1010 cfu/3 mL for 3 consecutive days. Total RNA [4 to 6 pigs/treatment on d 5; 5 to 7 pigs/treatment on 11 post-inoculation (PI)] was extracted from ileal mucosa to analyze gene expression profiles by Batch-Tag-Seq. The modulated differential gene expression were defined by 1.5-fold difference and a cutoff of P < 0.05 using limma-voom package. All processed data were statistically analyzed and evaluated by PANTHER classification system to determine the biological process function of genes in these lists. Compared to control, supplementation of recommended-dose AGP down-regulated genes related to inflammatory responses on d 5 and 11 PI; whereas, feeding low-dose AGP up-regulated genes associated with negative regulation of metabolic process on d 5, but down-regulated the genes related to immune responses on d 11 PI. The present observations support adverse effects of low-dose AGP in our previous study, indicated by exacerbated the detrimental effects of E. coli infection on pigs’ growth rate, diarrhea and systemic inflammation.

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 Convergent evolution of venom gland transcriptomes across Metazoa

2021 ◽  
Author(s):  
Giulia Zancolli ◽  
Maarten Reijnders ◽  
Robert Waterhouse ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venom Gland ◽  
Bioactive Molecules ◽  
Specific Gene ◽  
Animal Kingdom ◽  
Venom Glands

Animals have repeatedly evolved specialized organs and anatomical structures to produce and deliver a cocktail of potent bioactive molecules to subdue prey or predators: venom. This makes it one of the most widespread convergent functions in the animal kingdom. Whether animals have adopted the same genetic toolkit to evolved venom systems is a fascinating question that still eludes us. Here, we performed the first comparative analysis of venom gland transcriptomes from 20 venomous species spanning the main Metazoan lineages, to test whether different animals have independently adopted similar molecular mechanisms to perform the same function. We found a strong convergence in gene expression profiles, with venom glands being more similar to each other than to any other tissue from the same species, and their differences closely mirroring the species phylogeny. Although venom glands secrete some of the fastest evolving molecules (toxins), their gene expression does not evolve faster than evolutionarily older tissues. We found 15 venom gland specific gene modules enriched in endoplasmic reticulum stress and unfolded protein response pathways, indicating that animals have independently adopted stress response mechanisms to cope with mass production of toxins. This, in turns, activates regulatory networks for epithelial development, cell turnover and maintenance which seem composed of both convergent and lineage-specific factors, possibly reflecting the different developmental origins of venom glands. This study represents the first step towards an understanding of the molecular mechanisms underlying the repeated evolution of one of the most successful adaptive traits in the animal kingdom.

scholarly journals Universal attenuators and their interactions with feedback loops in gene regulatory networks

2016 ◽  
Author(s):  
Dianbo Liu ◽  
Luca Albergante ◽  
Timothy J Newman
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Feedback Loops ◽  
Human Cancer Cell ◽  
E Coli ◽  
Gene Regulatory

AbstractUsing a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analysing the preponderance of LRCs in the GRNs of E. coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as “universal attenuators”. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.In briefWe present a general principle that linear regulatory chains exponentially attenuate the range of expression in gene regulatory networks. The discovery of a universal interplay between linear regulatory chains and genetic feedback loops in microorganisms and a human cancer cell line is analysed and discussed.HighlightsWithin gene networks, linear regulatory chains act as exponentially strong attenuators of upstream variationBecause of their exponential behaviour, linear regulatory chains beyond a few genes provide no additional functionality and are rarely observed in gene networks across a range of different organismsNovel interactions between four-gene linear regulatory chains and feedback loops were discovered in E. coli, M. tuberculosis and human cancer cells, suggesting a universal mechanism of control.

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