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 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 A simple method to co-purify genomic DNA, RNA, and proteins for functional studies

2018 ◽  
Author(s):  
Jian Jiang ◽  
Junfei Ma ◽  
Bin Liu ◽  
Ying Wang
Keyword(s):  
Gene Expression ◽  
Molecular Biology ◽  
High Throughput ◽  
Genomic Dna ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Regulation Of Gene Expression ◽  
Robust Method ◽  
Simple Method ◽  
Functional Studies

AbstractUnderstanding the regulation of gene expression, from the epigenetic modifications on genomes to posttranscriptional and translational controls, are critical for elucidating molecular mechanisms underlying distinct phenotypes in biology. With the rapid development of Multi-Omics analyses, it is desirable to minimize sample variations by using DNA, RNA, and proteins co-purified from the same samples. Currently, most of the co-purification protocols rely on Tri Reagent (Trizol as a common representative) and require protein precipitation and dissolving steps, which render difficulties in experimental handling and high-throughput analyses. Here, we established a simple and robust method to minimize the precipitation steps and yield ready-to-use RNA and protein in solutions. This method can be applied to samples in small quantity, such as protoplasts. We demonstrated that the protoplast system equipped with this method may facilitate studies on viroid biogenesis. Given the ease and the robustness of this new method, it will have broad applications for plant research and other disciplines in molecular biology.

scholarly journals MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
He-Gang Chen ◽  
Xiong-Hui Zhou
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Drug Response ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Recent Decade ◽  
Data Set ◽  
Module Network ◽  
The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

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 Recent Advances in the Development of Exogenous dsRNA for the Induction of RNA Interference in Cancer Therapy

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 701
Author(s):  
Tatiana S. Golubeva ◽  
Viktoria A. Cherenko ◽  
Konstantin E. Orishchenko
Keyword(s):  
Gene Expression ◽  
Rna Interference ◽  
Molecular Biology ◽  
Gene Silencing ◽  
Regulation Of Gene Expression ◽  
Research Area ◽  
Disease Treatment ◽  
Practical Applications ◽  
Agricultural Plants ◽  
Insect Reproduction

Selective regulation of gene expression by means of RNA interference has revolutionized molecular biology. This approach is not only used in fundamental studies on the roles of particular genes in the functioning of various organisms, but also possesses practical applications. A variety of methods are being developed based on gene silencing using dsRNA—for protecting agricultural plants from various pathogens, controlling insect reproduction, and therapeutic techniques related to the oncological disease treatment. One of the main problems in this research area is the successful delivery of exogenous dsRNA into cells, as this can be greatly affected by the localization or origin of tumor. This overview is dedicated to describing the latest advances in the development of various transport agents for the delivery of dsRNA fragments for gene silencing, with an emphasis on cancer treatment.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals Genetic and epigenetic architecture of sex-biased expression in the jewel wasps Nasonia vitripennis and giraulti

2015 ◽  
Vol 112 (27) ◽  
pp. E3545-E3554 ◽  
Author(s):  
Xu Wang ◽  
John H. Werren ◽  
Andrew G. Clark
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Regulation Of Gene Expression ◽  
Sequence Divergence ◽  
Adult Males ◽  
Rna Seq ◽  
Functional Categories ◽  
Nasonia Vitripennis ◽  
Constitutive Gene Expression ◽  
Genome Bisulfite Sequencing

There is extraordinary diversity in sexual dimorphism (SD) among animals, but little is known about its epigenetic basis. To study the epigenetic architecture of SD in a haplodiploid system, we performed RNA-seq and whole-genome bisulfite sequencing of adult females and males from two closely related parasitoid wasps, Nasonia vitripennis and Nasonia giraulti. More than 75% of expressed genes displayed significantly sex-biased expression. As a consequence, expression profiles are more similar between species within each sex than between sexes within each species. Furthermore, extremely male- and female-biased genes are enriched for totally different functional categories: male-biased genes for key enzymes in sex-pheromone synthesis and female-biased genes for genes involved in epigenetic regulation of gene expression. Remarkably, just 70 highly expressed, extremely male-biased genes account for 10% of all transcripts in adult males. Unlike expression profiles, DNA methylomes are highly similar between sexes within species, with no consistent sex differences in methylation found. Therefore, methylation changes cannot explain the extensive level of sex-biased gene expression observed. Female-biased genes have smaller sequence divergence between species, higher conservation to other hymenopterans, and a broader expression range across development. Overall, female-biased genes have been recruited from genes with more conserved and broadly expressing “house-keeping” functions, whereas male-biased genes are more recently evolved and are predominately testis specific. In summary, Nasonia accomplish a striking degree of sex-biased expression without sex chromosomes or epigenetic differences in methylation. We propose that methylation provides a general signal for constitutive gene expression, whereas other sex-specific signals cause sex-biased gene expression.

Prediction and analysis of Metagenomic operons via MetaRon: a Pipeline for Prediction of Metagenomic OpeRons

2020 ◽  
Author(s):  
Syed Shujaat Ali Zaidi ◽  
Masood Ur Rehman Kayani ◽  
Xuegong Zhang ◽  
Imran Haider Shamsi
Keyword(s):  
Draft Genome ◽  
Gene Clusters ◽  
Transcriptional Unit ◽  
Metagenomic Data ◽  
Functional Information ◽  
Data Set ◽  
E Coli ◽  
Operon Prediction ◽  
K 12 ◽  
Gut Metagenome

Abstract Background: Efficient regulation of bacterial genes against the environmental stimulus results in unique operonic organizations. Lack of complete reference and functional information makes metagenomic operon prediction challenging and therefore opens new perspectives on the interpretation of the host-microbe interactions. Methods: Here we present MetaRon (pipeline for the prediction of Metagenomic operons), an open-source pipeline explicitly designed for the metagenomic shotgun sequencing data. It recreates the operonic structure without functional information. MetaRon identifies closely packed co-directional gene clusters with a promoter upstream and downstream of the first and last gene, respectively. Promoter prediction marks the transcriptional unit boundary (TUB) of closely packed co-directional gene clusters.Results: Escherichia coli (E. coli) K-12 MG1655 presents a gold standard for operon prediction. Therefore, MetaRon was initially implemented on two simulated illumina datasets: (1) E. coli MG1655 genome (2) a mixture of E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 168 genomes. Operons were predicted in the single genome and mixture of genomes with a sensitivity of 97.8% and 93.7%, respectively. In the next phase, operons predicted from E. coli c20 draft genome isolated from chicken gut metagenome achieved a sensitivity of 94.1%. Lastly, the application of MetaRon on 145 paired-end gut metagenome samples identified 1,232,407 unique operons. Conclusion: MetaRon removes two notable limitations of existing methods: (1) dependency on functional information, and (2) liberates the users from enormous metagenomic data management. Current study showed the idea of using operons as subset to represent the whole-metagenome in terms of secondary metabolites and demonstrated its effectiveness in explaining the occurrence of a disease condition. This will significantly reduce the hefty whole-metagenome data to a small more precise data set. Furthermore, metabolic pathways from the operonic sequences were identified in association with the occurrence of type 2 diabetes (T2D). Presumably, this is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case T2D. The application of MetaRon to metagenome data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

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